serial_no int64 1 24.2k | cuda_source stringlengths 11 9.01M |
|---|---|
18,701 | #include <stdio.h>
#include <malloc.h>
#include <stdlib.h>
#include <math.h>
#include <sys/time.h>
#include <cuda.h>
double wtime(void)
{
static struct timeval tv0;
double time_;
gettimeofday(&tv0,(struct timezone*)0);
time_=(double)((tv0.tv_usec + (tv0.tv_sec)*1000000));
return( time_/1000000);
}
void Mul(float *A, float *B, int hA, int wA, int wB, float *C)
{
int i,j,k;
for (i=0; i<hA; i++)
for (j=0; j<wB; j++){
C[i*wB+j] = 0.0;
for (k=0; k<wA; k++)
C[i*wB+j] += A[i*wA+k]*B[k*wB+j];
}
}
__global__ void cudaMul(float * A, float * B, int ha, int wa, int wb, float * C) {
int i = blockIdx.y * blockDim.y + threadIdx.y;
int j = blockIdx.x * blockDim.x + threadIdx.x;
if (i < ha && j < wb)
for (int k = 0; k < wa; k++)
C[i*wb+j] += A[i*wa+k]*B[k*wb+j];
}
void init_matrix(float *M, int hM, int wM, float k)
{
int i,j;
for (i=0; i<hM; i++)
for (j=0; j<wM; j++)
if (i==j)
M[i*wM+j] = k*1.0f;
else
M[i*wM+j] = -1.0f/(float)(wM);
}
void print_matrix(float *M, int hM, int wM)
{
int i,j;
for (i=0; i<hM; i++){
// printf("Line %i: ", i);
for (j=0; j<wM; j++)
printf("%4.1f ", M[i*wM+j]);
printf("\n");
}
}
int diff(float *A, float *B, int hA, int wA, int wB, float *C)
{
float *C_cpu;
int size_C = wB * hA;
C_cpu = (float*)malloc(size_C*sizeof(float));
int i,j,k;
double t0, t1;
t0 = wtime();
for (i=0; i<hA; i++)
for (j=0; j<wB; j++){
C_cpu[i*wB+j] = 0.0;
for (k=0; k<wA; k++){
C_cpu[i*wB+j] += A[i*wA+k]*B[k*wB+j];
}
}
t1 = wtime();
printf("Time CPU: %f\n", t1-t0);
//printf("\n\nMATRIX C_cpu\n");print_matrix(C_cpu, hA, wB);
for (i=0; i<hA; i++)
for (j=0; j<wB; j++)
if (fabsf(C_cpu[i*wB+j]-C[i*wB+j])>1e-5)
{
printf("[%i,%i]: %f!=%f\n", i, j, C_cpu[i*wB+j], C[i*wB+j]);
return(0);
}
return(1);
}
////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char** argv)
{
// Matrix variables
float *A, *B, *C;
float *A_GPU, *B_GPU, *C_GPU;
int hA, wA, hB, wB;
// int i;
setbuf(stdout, NULL);
if (argc!=4){
printf("./exec hA hB/WA wB\n");
exit(-1);
}
hA = atoi(argv[1]);
hB = wA = atoi(argv[2]);
wB = atoi(argv[3]);
// Init A and B, malloc C
int size_A = wA * hA;
A = (float*)malloc(size_A*sizeof(float));
init_matrix(A, hA, wA, 1.0);
int size_B = wB * hB;
B = (float*)malloc(size_B*sizeof(float));
init_matrix(B, hB, wB, 2.0);
// We will initialize C while cudaMemCpy works
int size_C = wB * hA;
cudaMalloc(&A_GPU, size_A*sizeof(float));
cudaMalloc(&B_GPU, size_B*sizeof(float));
cudaMalloc(&C_GPU, size_C*sizeof(float));
cudaMemcpy(A_GPU, A, size_A*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(B_GPU, B, size_B*sizeof(float), cudaMemcpyHostToDevice);
cudaMemset(C_GPU, 0.0f, size_C*sizeof(float));
C = (float*)malloc(size_C*sizeof(float));
// Mul(A, B, hA, wA, wB, C);
// printf("\n\nMATRIX A\n");print_matrix(A, hA, wA);
// printf("\n\nMATRIX B\n");print_matrix(B, hB, wB);
// printf("\n\nMATRIX C\n");print_matrix(C, hA, wB);
#define THX 32
#define THY 32
// Ahora sí que hacemos el calculo
dim3 b(THX, THY); // Threads por bloque
dim3 g(ceil(float(hA)/float(b.x)), ceil(float(wB)/float(b.y))); // Bloques por grid
printf("C_SIZE: %d x %d (%d), b: %d x %d, g: %d x %d\n", wB, hA, size_C, b.x, b.y, g.x, g.y);
double t0, t1;
t0 = wtime();
cudaMul<<<g, b>>>(A_GPU, B_GPU, hA, wA, wB, C_GPU);
cudaDeviceSynchronize();
t1 = wtime();
printf("Time GPU: %f\n", t1-t0);
cudaMemcpy(C, C_GPU, size_C*sizeof(float), cudaMemcpyDeviceToHost);
if (!diff(A, B, hA, wA, wB, C))
printf("ERROR=GPU.vs.CPU matrix mult differs\n");
else
printf("Everything went fine\n");
// print Matrix
// printf("\n\nMATRIX A\n");print_matrix(A, hA, wA);
// printf("\n\nMATRIX B\n");print_matrix(B, hB, wB);
// printf("\n\nMATRIX C\n");print_matrix(C, hA, wB);
free(A); free(B); free(C);
cudaFree(A_GPU);
cudaFree(B_GPU);
cudaFree(C_GPU);
return (1);
}
|
18,702 | #include <stdio.h>
__global__ void vector_add(const int *a, const int *b, int *c) {
*c = *a + *b;
}
int main(void) {
const int a = 2, b = 5;
int c = 0;
int *dev_a, *dev_b, *dev_c;
cudaMalloc((void **)&dev_a, sizeof(int));
cudaMalloc((void **)&dev_b, sizeof(int));
cudaMalloc((void **)&dev_c, sizeof(int));
cudaMemcpy(dev_a, &a, sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(dev_b, &b, sizeof(int), cudaMemcpyHostToDevice);
vector_add<<<1, 1>>>(dev_a, dev_b, dev_c);
cudaMemcpy(&c, dev_c, sizeof(int), cudaMemcpyDeviceToHost);
printf("%d + %d = %d, Is that right?\n", a, b, c);
cudaFree(dev_a);
cudaFree(dev_b);
cudaFree(dev_c);
return 0;
}
|
18,703 | #include "includes.h"
__global__ void Sign( float * x, size_t idx, size_t N)
{
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < N; i += blockDim.x * gridDim.x)
{
float res = x[(idx-1)*N+i];
if (res > 0 )
x[(idx-1)*N+i] = 1.0 ;
else if (res == 0)
x[(idx-1)*N+i] = 0.0;
else
x[(idx-1)*N+i] = -1.0 ;
}
return;
} |
18,704 | #include "includes.h"
__global__ void rectified_linear_backprop_upd_kernel( float4 * __restrict input_errors, const float4 * __restrict output_errors, const uint4 * __restrict bits_buffer, float negative_slope, bool add_update_to_destination, int elem_count)
{
int elem_id = blockDim.x * blockIdx.x + threadIdx.x;
if (elem_id < elem_count)
{
float4 val = output_errors[elem_id];
uint4 bits = bits_buffer[elem_id >> 5];
int lane_id = elem_id & 31;
unsigned int mask = (1 << lane_id);
if ((bits.x & mask) == 0)
val.x *= negative_slope;
if ((bits.y & mask) == 0)
val.y *= negative_slope;
if ((bits.z & mask) == 0)
val.z *= negative_slope;
if ((bits.w & mask) == 0)
val.w *= negative_slope;
if (add_update_to_destination)
{
float4 prv = input_errors[elem_id];
val.x += prv.x;
val.y += prv.y;
val.z += prv.z;
val.w += prv.w;
}
input_errors[elem_id] = val;
}
} |
18,705 | #include<stdio.h>
#include<iostream>
using namespace std;
void __global__ test() {
}
//struct false_usage{
// enum{
//
// }
//}
int main(int argc ,char* argv[]) {
if(argc < 2) {
fprintf(stderr,"invalid Usager,-c blocksize -g gridsize\n");
exit(-1);
}
int flag = 0;
unsigned int blocksize;
unsigned int gridsize;
while(++flag < argc) {
if(flag < argc && strlen(argv[flag]) == 2 && argv[flag][0] == '-') {
if (flag + 1 > argc) {
fprintf(stderr,"invalid Usager! input should be:``-c blocksize -g gridsize\n");
exit(-2);
}
switch(argv[flag][1] - 'a') {
case 'c' - 'a':
//protected
blocksize = atoi(argv[++flag]);
break;
case ('g' - 'a'):
gridsize = atoi(argv[++flag]);
break;
default:
fprintf(stderr, "no match\n");
}
}
}
dim3 block(blocksize, 1);
//dim3 grid(, 1);
}
|
18,706 |
/* This is a automatically generated test. Do not modify */
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
__global__
void compute(float comp, int var_1,float var_2,int var_3,int var_4,float var_5,float var_6,float var_7,float var_8,float* var_9,float var_10,float var_11,float var_12) {
for (int i=0; i < var_1; ++i) {
if (comp >= (var_2 / -1.0817E-36f)) {
comp = cosf(-1.9800E34f / (var_5 + ldexpf((-1.8078E-42f * +1.4986E-41f), 2)));
float tmp_1 = -1.4794E-35f;
float tmp_2 = -1.9424E-37f;
comp += tmp_2 / tmp_1 - (-1.3063E-18f - -1.0401E-35f * +1.2162E-43f - var_6);
if (comp >= +0.0f / var_7 / ceilf(+1.0071E-44f)) {
comp += log10f((+1.3650E-35f + var_8));
}
for (int i=0; i < var_3; ++i) {
comp += -1.2400E-37f + +1.4328E-42f;
var_9[i] = +1.6281E-15f;
float tmp_3 = +1.6286E36f;
comp = tmp_3 / var_9[i] * -1.9352E-41f * -1.4034E-44f + -1.6408E-13f + var_10 * +1.1786E7f;
}
for (int i=0; i < var_4; ++i) {
comp = (var_11 + var_12);
}
}
}
printf("%.17g\n", comp);
}
float* initPointer(float v) {
float *ret = (float*) malloc(sizeof(float)*10);
for(int i=0; i < 10; ++i)
ret[i] = v;
return ret;
}
int main(int argc, char** argv) {
/* Program variables */
float tmp_1 = atof(argv[1]);
int tmp_2 = atoi(argv[2]);
float tmp_3 = atof(argv[3]);
int tmp_4 = atoi(argv[4]);
int tmp_5 = atoi(argv[5]);
float tmp_6 = atof(argv[6]);
float tmp_7 = atof(argv[7]);
float tmp_8 = atof(argv[8]);
float tmp_9 = atof(argv[9]);
float* tmp_10 = initPointer( atof(argv[10]) );
float tmp_11 = atof(argv[11]);
float tmp_12 = atof(argv[12]);
float tmp_13 = atof(argv[13]);
compute<<<1,1>>>(tmp_1,tmp_2,tmp_3,tmp_4,tmp_5,tmp_6,tmp_7,tmp_8,tmp_9,tmp_10,tmp_11,tmp_12,tmp_13);
cudaDeviceSynchronize();
return 0;
}
|
18,707 | #include <cuda.h>
#include <assert.h>
#include <stdio.h>
template <int channel_per_thread, int filter_per_thread, int channel_per_block, int filter_per_block, int batch_per_block>
__global__ static void _cwc_kern_convolutional_backward_propagate_coefficient_default(const int strides, const int border, const int batch, const int batch_group_count,
float* input, const int rows, const int cols, const int channels_per_partition, const int partition,
float* out_grad, const int out_rows, const int out_cols,
float* coeff, const int filter_rows, const int filter_cols, const int count_per_partition)
{
assert(gridDim.x == strides);
assert(gridDim.y == filter_rows);
assert(gridDim.z * channel_per_block * filter_per_block * batch_per_block == count_per_partition * channels_per_partition * partition * batch);
assert(batch == batch_per_block * batch_group_count);
extern __shared__ float shared[];
float* shared_input = &shared[0];
float* shared_out_grad = &shared[channel_per_block * 3];
const int thidx = threadIdx.x + threadIdx.y * blockDim.x;
const int thcnt = blockDim.x * blockDim.y;
assert(blockDim.x * filter_per_thread == filter_per_block);
assert(blockDim.y * channel_per_thread == channel_per_block);
assert(thcnt >= channel_per_block);
assert(thcnt >= filter_per_block);
const int origin_x = blockIdx.x;
const int origin_y = blockIdx.y;
const int channel_group_count = channels_per_partition / channel_per_block;
const int filter_group_count = count_per_partition / filter_per_block;
const int partition_idx = blockIdx.z / (channel_group_count * filter_group_count * batch_group_count);
const int batch_group_idx = (blockIdx.z % (channel_group_count * filter_group_count * batch_group_count)) / (channel_group_count * filter_group_count);
const int filter_group_idx = (blockIdx.z % (channel_group_count * filter_group_count)) / channel_group_count;
const int channel_group_idx = blockIdx.z % channel_group_count;
const int start_x = max(origin_x - border, 0) - (origin_x - border);
const int end_x = min(out_cols, (cols + border - origin_x + strides - 1) / strides);
const int start_y = max(origin_y - border, 0) - (origin_y - border);
const int end_y = min(out_rows, (rows + border - origin_y + strides - 1) / strides);
input += (partition_idx * batch + batch_group_idx * batch_per_block) * rows * cols * channels_per_partition + (origin_y * cols + origin_x) * channels_per_partition + channel_group_idx * channel_per_block;
out_grad += (partition_idx * batch + batch_group_idx * batch_per_block) * out_rows * out_cols * count_per_partition + filter_group_idx * filter_per_block;
int i, j, k, c, x, y;
float prod[channel_per_thread][filter_per_thread][3];
#pragma unroll
for (i = 0; i < channel_per_thread; i++)
#pragma unroll
for (j = 0; j < filter_per_thread; j++)
#pragma unroll
for (k = 0; k < 3; k++)
prod[i][j][k] = 0;
__syncthreads();
for (c = 0; c < batch_per_block; c++)
{
for (y = start_y; y < end_y; y++)
{
#pragma unroll
for (k = 0; k < 2; k++)
if (thidx < channel_per_block)
shared_input[k * channel_per_block + thidx] = k * strides - border + origin_x < cols ? input[((y * strides - border) * cols + k * strides - border) * channels_per_partition + thidx] : 0;
for (x = start_x; x < end_x; x++)
{
if (thidx < filter_per_block)
shared_out_grad[thidx] = out_grad[(y * out_cols + x) * count_per_partition + thidx];
if (thidx < channel_per_block)
shared_input[((x - start_x + 2) % 3) * channel_per_block + thidx] = (x + 2) * strides - border + origin_x < cols ? input[((y * strides - border) * cols + (x + 2) * strides - border) * channels_per_partition + thidx] : 0;
__syncthreads();
#pragma unroll
for (k = 0; k < 3; k++)
#pragma unroll
for (i = 0; i < channel_per_thread; i++)
#pragma unroll
for (j = 0; j < filter_per_thread; j++)
prod[i][j][k] += shared_input[((x - start_x + k) % 3) * channel_per_block + i + threadIdx.y * channel_per_thread] * shared_out_grad[j + threadIdx.x * filter_per_thread];
__syncthreads();
}
}
input += rows * cols * channels_per_partition;
out_grad += out_rows * out_cols * count_per_partition;
}
const int cocnt = filter_cols * filter_rows * count_per_partition * partition;
coeff += cocnt * (channels_per_partition * batch_group_idx + channel_group_idx * channel_per_block) + (origin_y * filter_cols + origin_x) * count_per_partition * partition + partition_idx * count_per_partition + filter_group_idx * filter_per_block;
#pragma unroll
for (k = 0; k < 3; k++)
if (k * strides + origin_x < filter_cols)
#pragma unroll
for (i = 0; i < channel_per_thread; i++)
#pragma unroll
for (j = 0; j < filter_per_thread; j++)
coeff[(i + threadIdx.y * channel_per_thread) * cocnt + k * strides * count_per_partition * partition + j + threadIdx.x * filter_per_thread] = prod[i][j][k];
}
int main(int argc, char** argv)
{
float* in = 0;
float* out = 0;
cudaMalloc(&in, sizeof(float) * (55 * 55 * 96 * 128));
cudaMalloc(&out, sizeof(float) * (27 * 27 * 256 * 128));
float* in_host = 0;
float* out_host = 0;
int i, j, c, k;
cudaMallocHost(&in_host, sizeof(float) * 55 * 55 * 96 * 128);
for (i = 0; i < 55; i++)
for (j = 0; j < 55; j++)
for (c = 0; c < 96; c++)
for (k = 0; k < 128; k++)
in_host[i * 55 * 96 * 128 + j * 96 * 128 + c * 128 + k] = c * k;
cudaMemcpy(in, in_host, sizeof(float) * 55 * 55 * 96 * 128, cudaMemcpyHostToDevice);
cudaMallocHost(&out_host, sizeof(float) * 27 * 27 * 256 * 128);
for (i = 0; i < 27; i++)
for (j = 0; j < 27; j++)
for (c = 0; c < 256; c++)
for (k = 0; k < 128; k++)
out_host[i * 27 * 256 * 128 + j * 256 * 128 + c * 128 + k] = c * k;
cudaMemcpy(out, out_host, sizeof(float) * 27 * 27 * 256 * 128, cudaMemcpyHostToDevice);
float* w = 0;
dim3 thread_per_block(128 / 4, 16 / 4);
dim3 num_blocks(2, 5, (96 / 2 / 16) * (256 / 2 / 128) * 2 * 16);
cudaMalloc(&w, sizeof(float) * (256 * 96 / 2) * 5 * 5 * 16);
int shared_memory_size = sizeof(float) * (16 * 5 + 128);
cudaFuncSetCacheConfig(_cwc_kern_convolutional_backward_propagate_coefficient_default<4, 4, 16, 128, 8>, cudaFuncCachePreferShared);
_cwc_kern_convolutional_backward_propagate_coefficient_default
<4, 4, 16, 128, 8>
<<<num_blocks, thread_per_block, shared_memory_size>>>
(2, 1, 128, 16,
in, 55, 55, 96 / 2, 2,
out, 27, 27,
w, 5, 5, 256 / 2);
cudaFree(w);
cudaFree(out);
cudaFree(in);
cudaFreeHost(out_host);
cudaFreeHost(in_host);
return 0;
}
|
18,708 | float h_A[]= {
0.9955789127977188, 0.8561169145481113, 0.9886189102815928, 0.8374468724930431, 0.9685061234540727, 0.5495717202706307, 0.8972547277287506, 0.6807241267920705, 0.5165322394782044, 0.9307949057543159, 0.6428965371946282, 0.8579360303733771, 0.8439745945790106, 0.8225012250461169, 0.911510507690394, 0.7274223728579079, 0.861918363521842, 0.9398609537032636, 0.7978178818021956, 0.7035940570701562, 0.5510936426803046, 0.5777713387746117, 0.6575240502187609, 0.8019949969211984, 0.8726283657529055, 0.9944505617445121, 0.8674181038359936, 0.9118122932222659, 0.9458524370870459, 0.6104023991660895, 0.7173357955137594, 0.8093166956410347, 0.6938986793237286, 0.9347859988936603, 0.6835719455439768, 0.9858556941372807, 0.9972617979555756, 0.702004246013835, 0.6373706872429117, 0.582845916400164, 0.6119738915188204, 0.9702378655238367, 0.6353624609914853, 0.6940811602377381, 0.5271218336950849, 0.6293739741862483, 0.6839015460659787, 0.9803365640027856, 0.9648797256439594, 0.7829877499264047, 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0.80929307262447, 0.7729667929467965, 0.676365690860713, 0.5000531985185852, 0.6346024475102137, 0.7317308340726904, 0.912926239042649, 0.8295664943116009, 0.5088252652738128, 0.6007190059864002, 0.5083326202038352, 0.9971600088039801, 0.7401641577973674, 0.8653588901951355, 0.9340460546390924, 0.9567507190244409, 0.7513994310603697, 0.9262369500458061, 0.7364325049333439, 0.6624851214081735, 0.8578850031114578, 0.8472398606240645, 0.9489972496851354, 0.6917401064578721, 0.7060007394848418, 0.8940698369030433, 0.9914057172661465, 0.7703274418225492, 0.903534594610389, 0.6598532095939849, 0.5816278878489837, 0.7993572260743644, 0.9622774009462465, 0.6140051524892047, 0.935255688820293, 0.7734536294558596, 0.883647771712406, 0.8059392763621469, 0.958378038963594, 0.965724754013223, 0.5695239764740756, 0.6414781157791649, 0.8354654579839078, 0.7803517513081331, 0.6936281016785407, 0.6282474095280068, 0.9157110534291069, 0.6283115794930461, 0.644715381422331, 0.9608576968502532, 0.78158530965713, 0.9194300713320731, 0.6515884928866016, 0.7010548844099658, 0.7231396108901014, 0.8135706683604667, 0.7971069561650143, 0.7281784648625271, 0.6896815724381398, 0.575924928976591, 0.6793780452823677, 0.5442880003998949, 0.5177205376687266, 0.7697620607820738, 0.5450966691351896, 0.9147204897662816, 0.6749070480412991, 0.78836357841956, 0.7864114451865962, 0.5029957398429925, 0.8783244162998156, 0.6098862205092422, 0.6800562591005199, 0.8400479688038562, 0.5883130334162203, 0.8047904335741285, 0.9791459351457144, 0.7447994862920633, 0.5128166804770387, 0.9841108726181582, 0.7280281829561941, 0.8931550905001353, 0.5585287059248023, 0.5811818961218855, 0.551973816307147, 0.859443401629672, 0.765454624683529, 0.8073654855999726, 0.7459496062991913, 0.8845101369248363, 0.7768004051212979, 0.9454539871344806, 0.5151499380422586, 0.7783491282563478, 0.6166705853266351, 0.981676744823367, 0.6604756600549726, 0.7042384596822714, 0.8441855494895606, 0.5093586653706218, 0.8880889729088266, 0.6758961621781843, 0.7076652691044385, 0.8543899676613216, 0.6709818052573266, 0.586312481612858, 0.9679937337099749, 0.6724092360727478, 0.5645325752219214, 0.7495981459829574, 0.6591202953021345, 0.7317610713083476, 0.6171921171159587, 0.9625849074376687, 0.9925294710354544, 0.932975616392742, 0.9225360749213181, 0.8330179961970703, 0.8308275913974272, 0.6638708099665955, 0.6791932416037495, 0.953389063026844, 0.7796958477836804, 0.825159574684134, 0.6998122384124477, 0.5960938489571017, 0.8653373864845014, 0.9247611564070781, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0, 50000.0};
int h_B[]= {
1, 3, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82, 84, 86, 88, 90, 92, 94, 96, 98, 100, 102, 104, 106, 108, 110, 112, 114, 116, 118, 120, 122, 124, 126, 128, 130, 132, 134, 136, 138, 140, 142, 144, 146, 148, 150, 152, 154, 156, 158, 160, 162, 164, 166, 168, 170, 172, 174, 176, 178, 180, 182, 184, 186, 188, 190, 192, 194, 196, 198, 200, 202, 204, 206, 208, 210, 212, 214, 216, 218, 220, 222, 224, 226, 228, 230, 232, 234, 236, 238, 240, 242, 244, 246, 248, 250, 252, 254, 256, 258, 260, 262, 264, 266, 268, 270, 272, 274, 276, 278, 280, 282, 284, 287, 289, 291, 293, 295, 297, 299, 301, 303, 305, 307, 309, 311, 313, 315, 317, 319, 321, 323, 325, 327, 329, 331, 333, 335, 337, 339, 341, 343, 345, 347, 349, 351, 353, 355, 357, 359, 361, 363, 365, 367, 369, 371, 373, 375, 377, 379, 381, 383, 385, 387, 389, 391, 393, 396, 398, 400, 402, 404, 406, 408, 410, 412, 414, 416, 418, 420, 422, 424, 426, 428, 430, 432, 434, 436, 438, 440, 442, 444, 446, 448, 450, 452, 454, 456, 458, 460, 462, 464, 466, 468, 470, 472, 474, 476, 478, 480, 482, 484, 486, 488, 490, 493, 495, 497, 499, 502, 504, 506, 508, 510, 512, 514, 516, 518, 520, 522, 524, 526, 528, 530, 532, 534, 536, 538, 540, 542, 544, 546, 548, 550, 552, 554, 556, 559, 561, 563, 565, 567, 569, 572, 574, 578, 580, 582, 584, 586, 588, 590, 592, 594, 596, 598, 600, 602, 604, 606, 608, 610, 612, 614, 616, 618, 620, 622, 624, 626, 628, 630, 632, 634, 636, 638, 640, 642, 644, 646, 648, 650, 652, 654, 656, 658, 660, 662, 664, 666, 668, 670, 672, 674, 676, 678, 680, 682, 684, 686, 688, 690, 692, 694, 696, 699, 701, 704, 706, 708, 710, 712, 714, 716, 718, 720, 722, 724, 726, 728, 730, 732, 734, 736, 738, 740, 742, 744, 746, 748, 750, 752, 754, 756, 758, 760, 762, 764, 766, 768, 770, 772, 774, 776, 778, 780, 782, 784, 786, 788, 790, 792, 794, 796, 798, 800, 802, 804, 806, 808, 810, 812, 814, 816, 818, 820, 822, 824, 826, 828, 830, 832, 834, 836, 838, 840, 842, 845, 847, 849, 851, 853, 855, 858, 860, 862, 864, 866, 868, 870, 872, 874, 876, 878, 880, 882, 884, 886, 888, 890, 892, 894, 896, 898, 900, 902, 904, 906, 908, 910, 912, 915, 917, 919, 921, 923, 925, 927, 929, 931, 933, 935, 937, 939, 941, 943, 945, 949, 951, 953, 955, 957, 959, 961, 963, 965, 967, 969, 971, 973, 975, 977, 979, 981, 983, 985, 987, 989, 991, 993, 995, 997, 999, 1001, 1003, 1005, 1007, 1009, 1011, 1013, 1015, 1017, 1019, 1022, 1024, 1026, 1028, 1030, 1032, 1034, 1036, 1040, 1042, 1044, 1046, 1048, 1050, 1052, 1054, 1056, 1058, 1062, 1064, 1069, 1071, 1073, 1075, 1077, 1079, 1081, 1083, 1085, 1087, 1090, 1092, 1094, 1096, 1098, 1100, 1102, 1104, 1106, 1108, 1111, 1113, 1115, 1117, 1119, 1121, 1123, 1125, 1127, 1129, 1132, 1134, 1136, 1138, 1141, 1143, 1145, 1147, 1150, 1152, 1156, 1158, 1161, 1163, 1167, 1169, 1171, 1173, 1175, 1177, 1179, 1181, 1184, 1186, 1189, 1191, 1193, 1195, 1197, 1199, 1201, 1203, 1205, 1207, 1210, 1212, 1215, 1217, 1220, 1222, 1225, 1227, 1230, 1232, 1238, 1240, 1243, 1245, 1248, 1250, 1252, 1254, 1256, 1258, 1260, 1262, 1264, 1266, 1268, 1270, 1272, 1274, 1277, 1279, 1281, 1283, 1285, 1287, 1289, 1291, 1293, 1295, 1297, 1299, 1301, 1303, 1305, 1307, 1309, 1311, 1313, 1315, 1317, 1319, 1321, 1323, 1325, 1327, 1329, 1331, 1334, 1336, 1338, 1340, 1342, 1344, 1346, 1348, 1350, 1352, 1357, 1359, 1361, 1363, 1367, 1369, 1372, 1374, 1376, 1378, 1380, 1382, 1384, 1386, 1388, 1390, 1393, 1395, 1399, 1401, 1404, 1406, 1409, 1411, 1414, 1416, 1419, 1421, 1424, 1426, 1429, 1431, 1434, 1436, 1439, 1441, 1443, 1445, 1447, 1449, 1452, 1454, 1458, 1460, 1462, 1464, 1469, 1471, 1473, 1475, 1479, 1481, 1483, 1485, 1487, 1489, 1491, 1493, 1495, 1497, 1499, 1501, 1503, 1505, 1507, 1509, 1511, 1513, 1515, 1517, 1519, 1521, 1523, 1525, 1527, 1529, 1531, 1533, 1535, 1537, 1539, 1541, 1543, 1545, 1547, 1549, 1551, 1553, 1556, 1558, 1560, 1562, 1564, 1566, 1568, 1570, 1572, 1574, 1576, 1578, 1581, 1583, 1585, 1587, 1589, 1591, 1593, 1595, 1597, 1599, 1601, 1603, 1605, 1607, 1609, 1611, 1613, 1615, 1617, 1619, 1621, 1623, 1625, 1627, 1629, 1631, 1633, 1635, 1637, 1639, 1644, 1646, 1648, 1650, 1652, 1654, 1656, 1658, 1661, 1663, 1665, 1667, 1669, 1671, 1673, 1675, 1677, 1679, 1681, 1683, 1685, 1687, 1689, 1691, 1695, 1697, 1701, 1703, 1705, 1707, 1709, 1711, 1713, 1715, 1718, 1720, 1722, 1724, 1726, 1728, 1732, 1734, 1740, 1742, 1744, 1746, 1748, 1750, 1753, 1755, 1758, 1760, 1762, 1764, 1766, 1768, 1771, 1773, 1776, 1778, 1781, 1783, 1786, 1788, 1791, 1793, 1796, 1798, 1800, 1802, 1804, 1806, 1810, 1812, 1814, 1816, 1818, 1820, 1822, 1824, 1826, 1828, 1830, 1832, 1834, 1836, 1838, 1840, 1842, 1844, 1846, 1848, 1850, 1852, 1855, 1857, 1859, 1861, 1865, 1867, 1869, 1871, 1873, 1875, 1877, 1864, 1877, 1864, 1877, 1864, 1921, 1923, 1925, 1927, 1929, 1931, 1731, 1580, 1580, 1237, 1235, 1468, 1468, 1739, 1418, 1423, 1234, 1209, 1237, 1235, 1790, 1237, 1235, 1739, 1737, 1739, 1737, 1736, 1736, 1731, 1736, 1731, 1736, 1731, 1739, 1555, 1237, 1235, 1641, 1555, 1237, 1235, 1237, 1235, 1752, 1694, 1731, 1237, 1235, 1224, 1229, 1224, 1229, 1237, 1235, 1757, 1752, 1643, 1877, 1209, 1237, 1235, 1234, 1237, 1235, 1234, 1209, 1237, 1235, 1224, 1229, 1224, 1229, 1209, 1234, 1237, 1235, 1224, 1229, 1224, 1229, 1209, 1234, 1237, 1235, 1061, 1060, 1808, 1643, 1641, 1643, 1790, 1736, 1808, 1641, 1237, 1235, 1757, 1694, 2285, 2287, 2289, 2291, 2294, 2296, 2298, 2300, 2303, 2305, 2307, 2309, 2312, 2314, 2316, 2318, 2320, 2322, 2324, 2326, 2328, 2330, 2332, 2334, 2336, 2338, 2340, 2342, 2344, 2346, 2349, 2351, 2353, 2355, 2357, 2359, 1456, 1451, 2364, 2366, 2368, 2370, 2372, 2374, 2376, 2378, 2380, 2382, 2384, 2386, 2388, 2390, 2392, 2394, 1237, 1235, 1224, 1229, 1224, 1229, 1237, 1235, 1237, 1235, 1061, 1060, 1237, 1235, 1418, 1423, 1451, 1451, 1717, 1775, 1752, 1757, 1757, 1752, 1785, 1785, 1757, 1752, 1757, 1752, 1775, 1757, 1752, 1737, 1737, 1757, 1752, 1717, 2615, 2617, 2619, 2621, 2623, 2625, 2627, 2629, 2631, 2633, 2635, 2637, 2639, 2641, 2644, 2646, 2649, 2651, 2653, 2655, 1061, 1060, 1214, 1219, 1229, 1224, 1237, 1235, 1214, 1219, 1229, 1224, 1149, 1229, 1224, 1237, 1235, 1149, 1155, 1237, 1235, 1237, 1235, 1456, 1451, 1438, 1456, 1451, 1467, 1423, 1418, 1423, 1433, 1418, 1433, 1438, 1456, 1451, 1457, 1398, 1398, 1457, 1467, 1877, 1643, 1641, 1770, 1770, 1739, 1737, 1739, 1737, 1877, 1864, 1877, 1864, 1877, 1864, 1877, 1864, 1864, 1864, 2979, 2981, 2984, 2986, 2988, 2990, 2992, 2994, 2996, 2998, 3000, 3002, 3004, 3006, 3008, 3010, 3012, 3014, 3016, 3018, 3020, 3022, 3024, 3026, 3028, 3030, 3032, 3034, 3036, 3038, 3040, 3042, 3044, 3046, 3048, 3050, 3052, 3054, 3056, 3058, 3060, 3062, 3065, 3067, 3070, 3072, 3074, 3076, 3078, 3080, 3083, 3085, 3089, 3091, 3094, 3096, 3100, 3102, 3104, 3106, 3108, 3110, 3113, 3115, 3119, 3121, 3124, 3126, 3130, 3132, 3134, 3136, 3139, 3141, 3098, 3093, 3146, 3144, 3098, 3093, 3128, 3123, 3128, 3123, 3146, 3144, 2983, 2983, 3098, 3093, 3064, 3146, 3144, 3098, 3093, 3143, 3098, 3093, 3098, 3093, 3098, 3093, 3098, 3093, 3128, 3123, 3128, 3123, 3128, 3123, 3098, 3093, 3098, 3093, 3098, 3093, 3128, 3123, 3128, 3123, 3146, 3144, 3098, 3093, 3098, 3093, 3098, 3093, 3098, 3093, 3098, 3093, 3098, 3093, 3151, 3680, 3682, 3688, 3690, 3149, 3147, 3149, 3147, 3149, 3147, 2658, 3707, 3709, 3098, 3093, 3064, 3098, 3093, 3098, 3093, 3143, 2658, 2972, 2972, 4018, 4020, 3146, 3144, 4053, 4055, 4057, 4059, 4062, 4064, 3146, 3144, 3146, 3144, 3149, 3147, 3082, 3088, 3112, 3118, 3144, 3146, 3146, 3144, 3149, 3147, 3151, 4140, 4142, 4145, 4147, 4152, 4154, 4157, 4159, 4162, 4164, 4166, 4168, 4171, 4173, 4175, 4177, 4156, 4061, 4161, 4156, 4181, 4179, 4161, 4156, 4181, 4179, 4181, 4179, 4151, 4161, 4061, 4181, 4179, 4151, 4179, 4181, 4181, 4179, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 6592, 6594, 6596, 6598, 6600, 6602, 6604, 6606, 6608, 6610, 6612, 6614, 6616, 6618, 6620, 6622, 6624, 6626, 6628, 6630, 6632, 6634, 6636, 6638, 6640, 6642, 6644, 6646, 6648, 6650, 6652, 6654, 6656, 6658, 6660, 6662, 6664, 6666, 6668, 6670, 6672, 6674, 6676, 6678, 6680, 6682, 6684, 6686, 6688, 6690, 6692, 6694, 6696, 6698, 6700, 6702, 6704, 6706, 6708, 6710, 6712, 6714, 6716, 6718, 6720, 6722, 6724, 6726, 6728, 6730, 6732, 6734, 6736, 6738, 6740, 6742, 6744, 6746, 6748, 6750, 6752, 6754, 6756, 6758, 6760, 6762, 6764, 6766, 6768, 6770, 6772, 6774, 6776, 6778, 6780, 6782, 6784, 6786, 6788, 6790, 6792, 6794, 6796, 6798, 6800, 6802, 6804, 6806, 6808, 6810, 6812, 6814, 6816, 6818, 6820, 6822, 6824, 6826, 6828, 6830, 6832, 6834, 6836, 6838, 6840, 6842, 6844, 6846, 6848, 6850, 6852, 6854, 6856, 6858, 6860, 6862, 6864, 6866, 6868, 6870, 6872, 6874, 6876, 6878, 6880, 6882, 6884, 6886, 6888, 6890, 6892, 6894, 6896, 6898, 6900, 6902, 6904, 6906, 6908, 6910, 6912, 6914, 6916, 6918, 6920, 6922, 6924, 6926, 6928, 6930, 6932, 6934, 6936, 6938, 6940, 6942, 6944, 6946, 6948, 6950, 6952, 6954, 6956, 6958, 6960, 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17275, 17451, 17274, 17447, 17451, 17286, 17447, 17280, 17290, 17451, 17296, 17447, 17457, 17449, 17426, 17447, 17420, 17451, 17301, 17458, 17461, 17462, 17463, 17464, 17465, 17468, 17469, 17470, 17471, 17472, 17473, 17474, 17475, 17476, 17477, 17478, 17479, 17480, 17481, 17482, 17484, 17485, 17488, 17490, 17491, 17492, 17493, 17494, 17495, 17496, 17497, 17499, 17500, 17501, 17502, 17503, 17504, 17089, 16931, 17508, 17509, 17510, 17019, 17506, 17090, 16946, 17512, 17513, 17515, 16941, 17089, 17517, 17518, 17520, 17521, 17523, 17524, 17525, 17526, 17528, 17529, 17530, 17531, 17532, 17533, 17536, 17537, 17534, 17538, 17539, 17540, 17541, 17542, 17543, 17544, 17545, 17546, 17547, 17548, 17549, 17550, 17551, 17552, 17553, 17554, 17556, 17557, 17558, 17559, 17560, 17561, 17563, 17564, 17566, 17567, 17568, 17569, 17571, 17572, 17574, 17575, 17576, 17577, 17578, 17580, 17581, 17582, 17583, 17584, 17585, 17586, 17587, 17588, 17589, 17590, 17591, 17592, 17593, 17594, 17595, 17596, 17597, 17599, 17601, 17602, 17603, 17604, 17605, 17606, 17607, 17608, 17609, 17610, 17611, 17614, 17615, 17616, 17617, 17618, 17619, 17620, 17451, 17426, 17447, 17420, 17431, 17449, 17451, 17430, 17433, 17447, 17628, 17447, 17451, 17438, 17437, 17448, 17447, 17449, 17451, 17453, 17450, 17633, 15740, 15748, 15736, 15737, 15739, 15738, 15741, 15745, 15744, 15743, 15746, 15748, 15747, 15093, 15751, 15750, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 17664, 17665, 17666, 17669, 17670, 17671, 17674, 17675, 17676, 17679, 17681, 17682, 17683, 17684, 17685, 17686, 17687, 17688, 17689, 17691, 17692, 17693, 17695, 17696, 17697, 17698, 17699, 17703, 17704, 17705, 17706, 17707, 17708, 17709, 17710, 17711, 17712, 17714, 17355, 17716, 17717, 17719, 17720, 17721, 17722, 17723, 17725, 17726, 17727, 17728, 17731, 17732, 17733, 17735, 17736, 17738, 17739, 17740, 17741, 17744, 17745, 17746, 17747, 17748, 17749, 17750, 17751, 17754, 17756, 17757, 17758, 17759, 17760, 17761, 17762, 17763, 17766, 17768, 17769, 17770, 17771, 17772, 17773, 17774, 17775, 17776, 17777, 17778, 17779, 17780, 17781, 17782, 17783, 17784, 17786, 17787, 17788, 17789, 17790, 17791, 17793, 17795, 17797, 17467, 16868, 17800, 17802, 17805, 17808, 17810, 17483, 17814, 17089, 16905, 16911, 17487, 17816, 17818, 17820, 17823, 17824, 17826, 17828, 17830, 17831, 17835, 17832, 17837, 17838, 17842, 17843, 17840, 17516, 17845, 17846, 17522, 17849, 17851, 17854, 17858, 17861, 17863, 17867, 17871, 17875, 17555, 17879, 17881, 17883, 17885, 17886, 17888, 17570, 17573, 17893, 17895, 17579, 17900, 17904, 17907, 17911, 17913, 17598, 17600, 17918, 17922, 17924, 17926, 17613, 17930, 17933, 17934, 17935, 17936, 17937, 17938, 17939, 17940, 17941, 17942, 17943, 17945, 17946, 17947, 17948, 17949, 17950, 17951, 17952, 17953, 17954, 17956, 17957, 17958, 17959, 15080, 15740, 17960, 17961, 17962, 17963, 17964, 17965, 17966, 15749, 17967, 17968, 17969, 15752, 17970, 17971, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 17985, 17986, 17988, 17989, 17991, 17992, 17994, 17995, 17997, 18000, 18002, 18004, 18008, 18010, 18012, 18014, 18016, 18019, 18022, 18026, 18029, 18031, 18033, 18035, 18036, 18040, 18042, 18044, 18046, 18049, 18050, 18052, 18053, 17127, 18058, 18059, 18061, 18063, 17155, 18066, 18068, 18070, 18072, 18074, 18078, 18080, 18082, 18084, 18086, 18088, 18089, 16882, 18092, 18094, 18096, 18097, 18098, 18101, 18103, 18105, 18107, 17834, 18111, 18112, 17841, 18116, 18118, 18120, 16965, 18124, 17056, 16995, 18129, 18131, 18133, 18135, 18137, 18139, 17056, 18144, 18146, 18149, 18151, 17102, 18156, 18159, 18161, 18163, 18166, 18169, 18171, 18173, 18176, 18179, 18180, 15742, 18184, 18188, 18192, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18208, 18210, 18212, 18214, 18216, 18217, 18218, 18219, 18220, 18221, 18222, 18223, 18224, 18225, 18227, 18229, 18231, 18233, 18235, 18236, 18237, 18239, 18241, 18242, 18244, 18246, 17273, 18247, 17285, 18249, 17295, 18252, 18255, 18258, 18259, 18260, 18262, 18263, 18265, 18266, 17836, 18110, 18270, 18115, 18274, 18276, 17102, 18277, 18278, 18279, 17006, 18280, 18282, 18284, 17031, 17045, 18286, 18287, 17077, 18289, 18291, 17425, 18293, 17443, 18297, 18302, 18303, 18305, 18306, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18339, 18357, 18360, 18336, 18362, 18337, 18364, 18338, 18366, 18367, 18341, 18368, 18342, 18343, 17804, 18355, 18344, 18345, 18099, 18100, 18346, 18347, 18375, 18376, 18348, 18378, 18355, 18380, 18349, 17853, 18351, 18382, 18355, 17866, 18350, 17870, 18355, 18386, 18387, 18388, 18351, 18389, 18355, 18390, 18352, 18391, 18355, 18392, 18353, 18393, 18355, 18394, 18354, 18395, 18355, 17929, 18356, 18397, 18398, 18359, 18399, 18400, 18401, 18186, 18403, 18404, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18435, 18363, 18437, 18365, 18439, 18076, 18432, 18442, 18444, 18445, 18446, 18447, 18448, 18449, 18450, 18452, 18453, 18456, 18457, 18458, 18460, 18461, 18462, 17857, 18464, 18465, 18466, 18467, 18468, 17874, 18472, 18474, 18475, 18476, 18477, 18478, 18143, 18480, 18482, 17920, 18484, 18486, 18487, 18488, 18155, 18433, 18491, 18492, 18434, 18304, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18251, 18534, 18538, 18549, 18551, 18553, 18555, 18557, 18560, 18562, 18564, 18148, 18570, 18572, 18573, 18575, 18576, 18529, 18531, 18473, 18443, 18459, 18481, 18485, 18273, 18369, 18374, 18546, 18455, 18470, 18471, 18383, 18454, 18371, 18542, 18261, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18592, 18596, 18599, 18600, 18601, 18603, 18605, 18607, 18609, 18610, 18441, 18611, 18612, 18613, 18597, 18614, 18615, 18616, 18617, 18594, 18604, 18618, 18619, 18620, 18595, 18602, 18621, 18622, 18598, 18623, 18624, 18625, 18626, 18627, 18490, 18493, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18656, 18666, 18670, 18675, 18660, 18676, 18680, 18658, 18659, 18661, 18681, 18684, 18657, 18667, 18671, 18673, 18678, 18685, 18688, 18662, 18690, 18663, 18691, 18178, 18300, 25, 26, 27, 28, 29, 30, 31, 18720, 18724, 18727, 18728, 18729, 18732, 18669, 18725, 18730, 18683, 18734, 18739, 18741, 18497, 18743, 18744, 18496, 18494, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18723, 18726, 18755, 18757, 18733, 18761, 18765, 18175, 18183, 18187, 18191, 18768, 18301, 18769, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18784, 18736, 18786, 18787, 18788, 18791, 18792, 18793, 18794, 18796, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18816, 18818, 18790, 18822, 18767, 18824, 18825, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18820, 18849, 18850, 18852, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18880, 18882, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18577, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18944, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18854, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18913, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31};
int h_C[]= {
2, 4, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99, 101, 103, 105, 107, 109, 111, 113, 115, 117, 119, 121, 123, 125, 127, 129, 131, 133, 135, 137, 139, 141, 143, 145, 147, 149, 151, 153, 155, 157, 159, 161, 163, 165, 167, 169, 171, 173, 175, 177, 179, 181, 183, 185, 187, 189, 191, 193, 195, 197, 199, 201, 203, 205, 207, 209, 211, 213, 215, 217, 219, 221, 223, 225, 227, 229, 231, 233, 235, 237, 239, 241, 243, 245, 247, 249, 251, 253, 255, 257, 259, 261, 263, 265, 267, 269, 271, 273, 275, 277, 279, 281, 283, 285, 288, 290, 292, 294, 296, 298, 300, 302, 304, 306, 308, 310, 312, 314, 316, 318, 320, 322, 324, 326, 328, 330, 332, 334, 336, 338, 340, 342, 344, 346, 348, 350, 352, 354, 356, 358, 360, 362, 364, 366, 368, 370, 372, 374, 376, 378, 380, 382, 384, 386, 388, 390, 392, 394, 397, 399, 401, 403, 405, 407, 409, 411, 413, 415, 417, 419, 421, 423, 425, 427, 429, 431, 433, 435, 437, 439, 441, 443, 445, 447, 449, 451, 453, 455, 457, 459, 461, 463, 465, 467, 469, 471, 473, 475, 477, 479, 481, 483, 485, 487, 489, 491, 494, 496, 498, 500, 503, 505, 507, 509, 511, 513, 515, 517, 519, 521, 523, 525, 527, 529, 531, 533, 535, 537, 539, 541, 543, 545, 547, 549, 551, 553, 555, 557, 560, 562, 564, 566, 568, 570, 573, 575, 579, 581, 583, 585, 587, 589, 591, 593, 595, 597, 599, 601, 603, 605, 607, 609, 611, 613, 615, 617, 619, 621, 623, 625, 627, 629, 631, 633, 635, 637, 639, 641, 643, 645, 647, 649, 651, 653, 655, 657, 659, 661, 663, 665, 667, 669, 671, 673, 675, 677, 679, 681, 683, 685, 687, 689, 691, 693, 695, 697, 700, 702, 705, 707, 709, 711, 713, 715, 717, 719, 721, 723, 725, 727, 729, 731, 733, 735, 737, 739, 741, 743, 745, 747, 749, 751, 753, 755, 757, 759, 761, 763, 765, 767, 769, 771, 773, 775, 777, 779, 781, 783, 785, 787, 789, 791, 793, 795, 797, 799, 801, 803, 805, 807, 809, 811, 813, 815, 817, 819, 821, 823, 825, 827, 829, 831, 833, 835, 837, 839, 841, 843, 846, 848, 850, 852, 854, 856, 859, 861, 863, 865, 867, 869, 871, 873, 875, 877, 879, 881, 883, 885, 887, 889, 891, 893, 895, 897, 899, 901, 903, 905, 907, 909, 911, 913, 916, 918, 920, 922, 924, 926, 928, 930, 932, 934, 936, 938, 940, 942, 944, 946, 950, 952, 954, 956, 958, 960, 962, 964, 966, 968, 970, 972, 974, 976, 978, 980, 982, 984, 986, 988, 990, 992, 994, 996, 998, 1000, 1002, 1004, 1006, 1008, 1010, 1012, 1014, 1016, 1018, 1020, 1023, 1025, 1027, 1029, 1031, 1033, 1035, 1037, 1041, 1043, 1045, 1047, 1049, 1051, 1053, 1055, 1057, 1059, 1063, 1065, 1070, 1072, 1074, 1076, 1078, 1080, 1082, 1084, 1086, 1088, 1091, 1093, 1095, 1097, 1099, 1101, 1103, 1105, 1107, 1109, 1112, 1114, 1116, 1118, 1120, 1122, 1124, 1126, 1128, 1130, 1133, 1135, 1137, 1139, 1142, 1144, 1146, 1148, 1151, 1153, 1157, 1159, 1162, 1164, 1168, 1170, 1172, 1174, 1176, 1178, 1180, 1182, 1185, 1187, 1190, 1192, 1194, 1196, 1198, 1200, 1202, 1204, 1206, 1208, 1211, 1213, 1216, 1218, 1221, 1223, 1226, 1228, 1231, 1233, 1239, 1241, 1244, 1246, 1249, 1251, 1253, 1255, 1257, 1259, 1261, 1263, 1265, 1267, 1269, 1271, 1273, 1275, 1278, 1280, 1282, 1284, 1286, 1288, 1290, 1292, 1294, 1296, 1298, 1300, 1302, 1304, 1306, 1308, 1310, 1312, 1314, 1316, 1318, 1320, 1322, 1324, 1326, 1328, 1330, 1332, 1335, 1337, 1339, 1341, 1343, 1345, 1347, 1349, 1351, 1353, 1358, 1360, 1362, 1364, 1368, 1370, 1373, 1375, 1377, 1379, 1381, 1383, 1385, 1387, 1389, 1391, 1394, 1396, 1400, 1402, 1405, 1407, 1410, 1412, 1415, 1417, 1420, 1422, 1425, 1427, 1430, 1432, 1435, 1437, 1440, 1442, 1444, 1446, 1448, 1450, 1453, 1455, 1459, 1461, 1463, 1465, 1470, 1472, 1474, 1476, 1480, 1482, 1484, 1486, 1488, 1490, 1492, 1494, 1496, 1498, 1500, 1502, 1504, 1506, 1508, 1510, 1512, 1514, 1516, 1518, 1520, 1522, 1524, 1526, 1528, 1530, 1532, 1534, 1536, 1538, 1540, 1542, 1544, 1546, 1548, 1550, 1552, 1554, 1557, 1559, 1561, 1563, 1565, 1567, 1569, 1571, 1573, 1575, 1577, 1579, 1582, 1584, 1586, 1588, 1590, 1592, 1594, 1596, 1598, 1600, 1602, 1604, 1606, 1608, 1610, 1612, 1614, 1616, 1618, 1620, 1622, 1624, 1626, 1628, 1630, 1632, 1634, 1636, 1638, 1640, 1645, 1647, 1649, 1651, 1653, 1655, 1657, 1659, 1662, 1664, 1666, 1668, 1670, 1672, 1674, 1676, 1678, 1680, 1682, 1684, 1686, 1688, 1690, 1692, 1696, 1698, 1702, 1704, 1706, 1708, 1710, 1712, 1714, 1716, 1719, 1721, 1723, 1725, 1727, 1729, 1733, 1735, 1741, 1743, 1745, 1747, 1749, 1751, 1754, 1756, 1759, 1761, 1763, 1765, 1767, 1769, 1772, 1774, 1777, 1779, 1782, 1784, 1787, 1789, 1792, 1794, 1797, 1799, 1801, 1803, 1805, 1807, 1811, 1813, 1815, 1817, 1819, 1821, 1823, 1825, 1827, 1829, 1831, 1833, 1835, 1837, 1839, 1841, 1843, 1845, 1847, 1849, 1851, 1853, 1856, 1858, 1860, 1862, 1866, 1868, 1870, 1872, 1874, 1876, 1863, 1863, 1854, 1854, 1863, 1863, 1922, 1924, 1926, 1928, 1930, 1932, 286, 1477, 1660, 1236, 1236, 1477, 1660, 1730, 1276, 1276, 492, 492, 1236, 1236, 948, 1236, 1236, 1730, 1730, 1738, 1738, 286, 571, 571, 571, 571, 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16416, 14351, 6142, 16422, 16422, 16421, 6150, 6152, 14927, 6154, 14372, 14374, 6157, 14943, 6159, 14383, 6162, 14387, 6196, 6197, 6199, 6200, 6207, 6208, 6216, 6217, 6218, 6229, 6230, 6231, 6232, 6234, 6235, 16167, 16175, 16211, 16209, 16190, 16188, 15592, 15596, 16201, 16202, 16204, 16206, 16211, 16209, 15621, 15612, 16563, 16223, 16241, 16238, 15645, 15649, 15654, 15661, 15668, 15667, 15682, 15677, 6441, 6442, 6443, 6444, 6445, 6447, 6448, 6450, 6451, 6453, 6454, 6462, 6463, 6472, 6473, 6475, 6476, 6478, 6479, 6480, 6481, 6489, 6490, 6499, 6500, 6502, 6503, 6505, 6506, 6507, 6508, 25, 26, 27, 28, 29, 30, 31, 15786, 15802, 15821, 15841, 15857, 15879, 15895, 15913, 15934, 15971, 15996, 16013, 16043, 16070, 16088, 16109, 16131, 16136, 16141, 5480, 5481, 5486, 5487, 5488, 5490, 16800, 5494, 5496, 5504, 5505, 16808, 16811, 5513, 5514, 5515, 5517, 5519, 5520, 5522, 5531, 5532, 16826, 5539, 5540, 5541, 5542, 5544, 5546, 5548, 5561, 5562, 5563, 16846, 5573, 5593, 5594, 16851, 5614, 5615, 16863, 5621, 5629, 5630, 16874, 5638, 5640, 5641, 5646, 5647, 16887, 5654, 5655, 16892, 5666, 5667, 16900, 5675, 5676, 5682, 5683, 5684, 5685, 5687, 5688, 16915, 5701, 5702, 16925, 5726, 5728, 5729, 16936, 16940, 16943, 16945, 5749, 5750, 16951, 5769, 5770, 16957, 5778, 5780, 5781, 5786, 5787, 16970, 5790, 16777, 5792, 5798, 5799, 16976, 5809, 5811, 5812, 5815, 5816, 16987, 5826, 5827, 5828, 5831, 5832, 16999, 5837, 5839, 5840, 5865, 5866, 17015, 5885, 5886, 17024, 5890, 5892, 5893, 5894, 5901, 5902, 17036, 5912, 5913, 5914, 5921, 5922, 17051, 5925, 5927, 5928, 5934, 5935, 17061, 5955, 5956, 17070, 5960, 5961, 5963, 5964, 5972, 5973, 17083, 5996, 5997, 17094, 6006, 6007, 6008, 6051, 6052, 17108, 6059, 6061, 6063, 6064, 6065, 6066, 6068, 6078, 6079, 6080, 6089, 6091, 17132, 6094, 6116, 6117, 17137, 6124, 6126, 6127, 6129, 6130, 6132, 6133, 6143, 6144, 6145, 6153, 6155, 6156, 6158, 6160, 17165, 6163, 17168, 17170, 17172, 17174, 17177, 17179, 17181, 16856, 16170, 16169, 16854, 16169, 6243, 6244, 16858, 16854, 16177, 16178, 16178, 16176, 16180, 16179, 16180, 16181, 16888, 16183, 16184, 16182, 16184, 6266, 16895, 16186, 6269, 6271, 16191, 6274, 16513, 16191, 16194, 16195, 16920, 16195, 16193, 15599, 6287, 16196, 15599, 16198, 16199, 15595, 16196, 6294, 6295, 6296, 16200, 16203, 16203, 6305, 16205, 16208, 6309, 16208, 6314, 16952, 16211, 6317, 16214, 16213, 6320, 15617, 15620, 15620, 16215, 6325, 16214, 16218, 16216, 16218, 16217, 16219, 6336, 6337, 16219, 16222, 16225, 16224, 16978, 16226, 16225, 16229, 16228, 16227, 16229, 16230, 16232, 16575, 16232, 17000, 17008, 17008, 17007, 6363, 17010, 16237, 17011, 16236, 17009, 16237, 6370, 17010, 16246, 6373, 16244, 15644, 16245, 15644, 6378, 16246, 15648, 6382, 15658, 15657, 16249, 16248, 16247, 6388, 16249, 15658, 17163, 17032, 17163, 17078, 17047, 17039, 17047, 17046, 16259, 16258, 16260, 16258, 16263, 16264, 15669, 16261, 6414, 15674, 6416, 16264, 15674, 17079, 17163, 17163, 17078, 16271, 15681, 16273, 15678, 16273, 6431, 6432, 16269, 15681, 16274, 16275, 16275, 16276, 17211, 17213, 17216, 17218, 17220, 17222, 17224, 17226, 17228, 17230, 17232, 17234, 17236, 17238, 17240, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 17267, 17263, 17269, 17271, 17272, 17277, 17263, 17282, 17283, 17284, 17288, 17263, 17292, 16834, 16836, 17297, 17299, 17265, 17265, 17264, 17266, 17265, 17303, 17248, 17306, 17249, 16867, 17310, 17250, 16879, 17314, 17316, 17319, 17251, 17322, 17252, 17325, 17327, 17329, 17331, 17253, 17334, 17254, 17338, 17255, 17344, 17347, 17256, 16962, 17351, 17353, 5791, 17359, 16981, 17363, 17365, 17257, 17368, 17371, 17002, 17375, 17377, 17258, 17380, 17382, 17383, 17385, 17387, 17259, 17389, 17391, 17393, 17053, 17397, 17399, 17260, 17402, 17404, 17074, 17407, 17409, 17261, 17412, 17262, 17415, 17418, 17263, 17421, 17422, 17424, 17427, 17429, 17265, 17266, 17265, 17265, 17264, 17435, 17263, 17145, 17441, 17442, 17444, 17446, 17264, 17265, 17265, 17265, 17266, 16803, 17163, 16801, 17163, 17163, 16821, 17163, 16812, 16829, 17163, 16838, 17163, 17175, 17163, 17120, 17163, 17111, 17163, 17300, 17459, 6238, 6239, 6240, 6241, 6242, 6245, 6246, 6248, 6249, 6250, 6251, 6254, 6256, 6257, 6258, 6260, 6261, 6262, 6263, 6264, 6267, 6268, 6272, 6275, 6276, 6280, 6281, 6282, 6283, 6284, 6286, 6288, 6289, 6290, 6291, 6292, 6293, 17336, 17336, 6299, 6300, 6301, 17336, 17507, 17341, 17342, 6307, 6308, 6310, 17340, 17341, 6315, 6316, 6318, 6319, 6321, 6322, 6323, 6324, 6326, 6328, 6330, 6331, 6332, 6334, 6338, 6339, 17535, 6341, 6342, 6343, 6344, 6345, 6346, 6348, 6349, 6350, 6352, 6354, 6355, 6356, 6358, 6360, 6361, 6362, 6364, 6365, 6366, 6367, 6368, 6369, 6371, 6372, 6374, 6375, 6376, 6377, 6379, 6380, 6383, 6384, 6385, 6386, 6387, 6389, 6390, 6393, 6394, 6395, 6396, 6399, 6400, 6401, 6402, 6404, 6405, 6407, 6408, 6410, 6411, 6412, 6413, 6415, 6417, 6418, 6420, 6421, 6422, 6424, 6426, 6427, 6428, 6429, 6430, 6433, 6434, 6436, 6438, 6439, 6440, 17621, 17163, 17120, 17163, 17111, 17130, 17163, 17163, 17128, 17432, 17163, 17629, 17163, 17163, 17142, 17140, 17158, 17163, 17163, 17163, 17452, 17161, 17634, 17625, 17455, 17454, 17456, 17632, 17631, 17460, 17624, 17623, 17622, 17625, 17627, 17626, 17630, 17632, 17631, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 17268, 5482, 17667, 17278, 5508, 17672, 17289, 5535, 17677, 17680, 5564, 5566, 5567, 5568, 5570, 17304, 5597, 17307, 5618, 17311, 5633, 17694, 17317, 17320, 5658, 17323, 5670, 17332, 5691, 17335, 5705, 17339, 5732, 17345, 17348, 5773, 17713, 17354, 17715, 17360, 17718, 17366, 5819, 17369, 17372, 17724, 17378, 5869, 17381, 17729, 17388, 5905, 17734, 17394, 17737, 17400, 5938, 17403, 17742, 17410, 5976, 17413, 6000, 17416, 17419, 6055, 17752, 17755, 6081, 6083, 6084, 6086, 6087, 17436, 6120, 17764, 17767, 6146, 6147, 6148, 6149, 6151, 6191, 6192, 6193, 6194, 6202, 6203, 6204, 6205, 6211, 6212, 6213, 6215, 6221, 6222, 6223, 6224, 6225, 6226, 17794, 17796, 17466, 17798, 17690, 17801, 17803, 17806, 17809, 17811, 17813, 17486, 17701, 17700, 17702, 17815, 17817, 17819, 17821, 17498, 17825, 17827, 17829, 6297, 6298, 6302, 17833, 6304, 6306, 6311, 6312, 17514, 17844, 17519, 17847, 17848, 17850, 17527, 17855, 17859, 17862, 17864, 17868, 17872, 17876, 17878, 17880, 17882, 17562, 17565, 17887, 17889, 17890, 17892, 17894, 17896, 17897, 17901, 17905, 17908, 17912, 17914, 17915, 17916, 17919, 17923, 17925, 17612, 17927, 17931, 17214, 6457, 6459, 6460, 6461, 6466, 6467, 6468, 6469, 6470, 6471, 6485, 6486, 6487, 6488, 6493, 6494, 6495, 6496, 6497, 6498, 6510, 6512, 6513, 6515, 17785, 17792, 6520, 6521, 6522, 6551, 6552, 6553, 6556, 17944, 6559, 6560, 6562, 17955, 6565, 6566, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 16791, 17668, 16809, 17673, 16827, 17678, 16844, 17996, 16845, 16852, 16864, 16875, 16893, 16901, 16916, 16926, 16937, 16958, 17357, 16988, 17005, 17016, 17730, 17037, 17044, 17062, 17743, 17084, 17095, 17109, 17753, 17126, 18054, 18055, 17138, 17765, 18062, 18064, 18065, 18067, 18069, 18071, 18073, 18075, 18079, 18081, 18083, 18085, 18087, 6252, 18090, 18005, 18093, 18095, 6273, 6277, 6278, 18102, 18104, 18106, 18108, 18109, 17511, 17839, 18113, 18117, 18119, 18121, 18020, 18125, 18024, 18027, 18130, 18132, 18134, 18136, 18138, 18140, 18038, 18145, 18147, 18150, 18152, 18047, 18157, 18160, 18162, 18164, 18167, 18170, 18172, 18174, 18177, 6517, 6519, 18154, 18185, 6558, 6564, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 16792, 17279, 16828, 18215, 17998, 16853, 16865, 16876, 16894, 16902, 16917, 16927, 16938, 16959, 16989, 17017, 17038, 17063, 17085, 17096, 17110, 18240, 18056, 17139, 18245, 17156, 18209, 18248, 18211, 18250, 18213, 18253, 18256, 18257, 6255, 17812, 17489, 18264, 17822, 18267, 18268, 18269, 18271, 18272, 18275, 6329, 18226, 17865, 6347, 6353, 18228, 18281, 18283, 18285, 18230, 18232, 6406, 18288, 18234, 18290, 6437, 18238, 18294, 18243, 18298, 18181, 6554, 18189, 18193, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18340, 18358, 18361, 17984, 6195, 17987, 6206, 17990, 6214, 18254, 17999, 17799, 18001, 18003, 18370, 18006, 18007, 18009, 18372, 18373, 18011, 18013, 17505, 18377, 18015, 18379, 18017, 17852, 18018, 18381, 18021, 6335, 18023, 18384, 18025, 18385, 18028, 6359, 17884, 17891, 18030, 17898, 18032, 6392, 18034, 6398, 18037, 17909, 18039, 17917, 18041, 6423, 18043, 17928, 18045, 18396, 18048, 6458, 18295, 18057, 6484, 18299, 18182, 18402, 18190, 18194, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 6190, 18436, 6201, 18438, 6210, 18440, 17993, 6237, 6247, 6253, 18091, 6259, 6265, 6270, 18451, 6279, 6285, 6303, 18114, 6313, 6327, 18122, 6333, 18463, 6340, 18126, 6351, 18127, 6357, 18469, 6381, 6391, 17899, 6397, 17903, 6403, 18479, 6409, 6419, 18483, 6425, 6435, 18153, 6456, 18489, 18051, 6483, 18165, 18060, 18495, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18533, 6220, 17807, 17856, 17860, 17869, 17873, 18128, 18141, 18142, 17910, 18567, 17932, 18292, 6465, 18296, 6492, 18528, 18530, 18558, 18535, 18558, 18565, 18568, 18547, 18536, 18543, 18545, 18550, 18558, 18558, 18552, 18544, 18539, 18541, 18540, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18077, 18123, 17877, 17902, 17906, 17921, 18158, 18168, 6511, 6514, 18593, 6523, 6524, 6525, 18563, 6527, 6528, 6529, 6530, 18537, 18569, 6534, 6535, 6536, 18548, 18563, 6542, 6543, 18554, 6545, 6546, 6548, 6549, 6550, 18606, 18608, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18532, 6518, 6526, 6531, 18561, 6533, 6537, 18556, 18559, 18566, 6541, 6544, 18550, 18668, 18672, 18674, 18679, 18686, 18689, 18571, 6557, 18574, 6563, 18665, 18664, 25, 26, 27, 28, 29, 30, 31, 6516, 6532, 6538, 6539, 6540, 6547, 18722, 18677, 18682, 18731, 18735, 6555, 6561, 18742, 6570, 6571, 18740, 18721, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18753, 18754, 18756, 18687, 18758, 18737, 6567, 18763, 18763, 18763, 18764, 6574, 18752, 6576, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18759, 18785, 18760, 18738, 18762, 6568, 6569, 6572, 6573, 6575, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18817, 18789, 18821, 18766, 18823, 18795, 18797, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18848, 18819, 18851, 18853, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18881, 18883, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18912, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 6577, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 18976, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 19008, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31};
bool h_Op[]= {
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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
#define THREADS_PER_BLOCK 32
#define BLOCKS_PER_GRID 1
#define SIZE_OF_IN 6592
#define SIZE_OF_AC 12480
__device__ void
ac(float *A, const int *B, const int *C, const bool *Op, int n_iter) {
int i= blockDim.x * blockIdx.x + threadIdx.x;
__shared__ float R[596*THREADS_PER_BLOCK];
const int t= THREADS_PER_BLOCK;
__shared__ float final;
final=0;
R[i + 0*t] = A[i + 0*t];
R[i + 1*t] = A[i + 1*t];
R[i + 2*t] = A[i + 2*t];
R[i + 3*t] = A[i + 3*t];
R[i + 4*t] = A[i + 4*t];
R[i + 5*t] = A[i + 5*t];
R[i + 6*t] = A[i + 6*t];
R[i + 7*t] = A[i + 7*t];
R[i + 8*t] = A[i + 8*t];
R[i + 9*t] = A[i + 9*t];
R[i + 10*t] = A[i + 10*t];
R[i + 11*t] = A[i + 11*t];
R[i + 12*t] = A[i + 12*t];
R[i + 13*t] = A[i + 13*t];
R[i + 14*t] = A[i + 14*t];
R[i + 15*t] = A[i + 15*t];
R[i + 16*t] = A[i + 16*t];
R[i + 17*t] = A[i + 17*t];
R[i + 18*t] = A[i + 18*t];
R[i + 19*t] = A[i + 19*t];
R[i + 20*t] = A[i + 20*t];
R[i + 21*t] = A[i + 21*t];
R[i + 22*t] = A[i + 22*t];
R[i + 23*t] = A[i + 23*t];
R[i + 24*t] = A[i + 24*t];
R[i + 25*t] = A[i + 25*t];
R[i + 26*t] = A[i + 26*t];
R[i + 27*t] = A[i + 27*t];
R[i + 28*t] = A[i + 28*t];
R[i + 29*t] = A[i + 29*t];
R[i + 30*t] = A[i + 30*t];
R[i + 31*t] = A[i + 31*t];
R[i + 32*t] = A[i + 32*t];
R[i + 33*t] = A[i + 33*t];
R[i + 34*t] = A[i + 34*t];
R[i + 35*t] = A[i + 35*t];
R[i + 36*t] = A[i + 36*t];
R[i + 37*t] = A[i + 37*t];
R[i + 38*t] = A[i + 38*t];
R[i + 39*t] = A[i + 39*t];
R[i + 40*t] = A[i + 40*t];
R[i + 41*t] = A[i + 41*t];
R[i + 42*t] = A[i + 42*t];
R[i + 43*t] = A[i + 43*t];
R[i + 44*t] = A[i + 44*t];
R[i + 45*t] = A[i + 45*t];
R[i + 46*t] = A[i + 46*t];
R[i + 47*t] = A[i + 47*t];
R[i + 48*t] = A[i + 48*t];
R[i + 49*t] = A[i + 49*t];
R[i + 50*t] = A[i + 50*t];
R[i + 51*t] = A[i + 51*t];
R[i + 52*t] = A[i + 52*t];
R[i + 53*t] = A[i + 53*t];
R[i + 54*t] = A[i + 54*t];
R[i + 55*t] = A[i + 55*t];
R[i + 56*t] = A[i + 56*t];
R[i + 57*t] = A[i + 57*t];
R[i + 58*t] = A[i + 58*t];
R[i + 59*t] = A[i + 59*t];
R[i + 60*t] = A[i + 60*t];
R[i + 61*t] = A[i + 61*t];
R[i + 62*t] = A[i + 62*t];
R[i + 63*t] = A[i + 63*t];
R[i + 64*t] = A[i + 64*t];
R[i + 65*t] = A[i + 65*t];
R[i + 66*t] = A[i + 66*t];
R[i + 67*t] = A[i + 67*t];
R[i + 68*t] = A[i + 68*t];
R[i + 69*t] = A[i + 69*t];
R[i + 70*t] = A[i + 70*t];
R[i + 71*t] = A[i + 71*t];
R[i + 72*t] = A[i + 72*t];
R[i + 73*t] = A[i + 73*t];
R[i + 74*t] = A[i + 74*t];
R[i + 75*t] = A[i + 75*t];
R[i + 76*t] = A[i + 76*t];
R[i + 77*t] = A[i + 77*t];
R[i + 78*t] = A[i + 78*t];
R[i + 79*t] = A[i + 79*t];
R[i + 80*t] = A[i + 80*t];
R[i + 81*t] = A[i + 81*t];
R[i + 82*t] = A[i + 82*t];
R[i + 83*t] = A[i + 83*t];
R[i + 84*t] = A[i + 84*t];
R[i + 85*t] = A[i + 85*t];
R[i + 86*t] = A[i + 86*t];
R[i + 87*t] = A[i + 87*t];
R[i + 88*t] = A[i + 88*t];
R[i + 89*t] = A[i + 89*t];
R[i + 90*t] = A[i + 90*t];
R[i + 91*t] = A[i + 91*t];
R[i + 92*t] = A[i + 92*t];
R[i + 93*t] = A[i + 93*t];
R[i + 94*t] = A[i + 94*t];
R[i + 95*t] = A[i + 95*t];
R[i + 96*t] = A[i + 96*t];
R[i + 97*t] = A[i + 97*t];
R[i + 98*t] = A[i + 98*t];
R[i + 99*t] = A[i + 99*t];
R[i + 100*t] = A[i + 100*t];
R[i + 101*t] = A[i + 101*t];
R[i + 102*t] = A[i + 102*t];
R[i + 103*t] = A[i + 103*t];
R[i + 104*t] = A[i + 104*t];
R[i + 105*t] = A[i + 105*t];
R[i + 106*t] = A[i + 106*t];
R[i + 107*t] = A[i + 107*t];
R[i + 108*t] = A[i + 108*t];
R[i + 109*t] = A[i + 109*t];
R[i + 110*t] = A[i + 110*t];
R[i + 111*t] = A[i + 111*t];
R[i + 112*t] = A[i + 112*t];
R[i + 113*t] = A[i + 113*t];
R[i + 114*t] = A[i + 114*t];
R[i + 115*t] = A[i + 115*t];
R[i + 116*t] = A[i + 116*t];
R[i + 117*t] = A[i + 117*t];
R[i + 118*t] = A[i + 118*t];
R[i + 119*t] = A[i + 119*t];
R[i + 120*t] = A[i + 120*t];
R[i + 121*t] = A[i + 121*t];
R[i + 122*t] = A[i + 122*t];
R[i + 123*t] = A[i + 123*t];
R[i + 124*t] = A[i + 124*t];
R[i + 125*t] = A[i + 125*t];
R[i + 126*t] = A[i + 126*t];
R[i + 127*t] = A[i + 127*t];
R[i + 128*t] = A[i + 128*t];
R[i + 129*t] = A[i + 129*t];
R[i + 130*t] = A[i + 130*t];
R[i + 131*t] = A[i + 131*t];
R[i + 132*t] = A[i + 132*t];
R[i + 133*t] = A[i + 133*t];
R[i + 134*t] = A[i + 134*t];
R[i + 135*t] = A[i + 135*t];
R[i + 136*t] = A[i + 136*t];
R[i + 137*t] = A[i + 137*t];
R[i + 138*t] = A[i + 138*t];
R[i + 139*t] = A[i + 139*t];
R[i + 140*t] = A[i + 140*t];
R[i + 141*t] = A[i + 141*t];
R[i + 142*t] = A[i + 142*t];
R[i + 143*t] = A[i + 143*t];
R[i + 144*t] = A[i + 144*t];
R[i + 145*t] = A[i + 145*t];
R[i + 146*t] = A[i + 146*t];
R[i + 147*t] = A[i + 147*t];
R[i + 148*t] = A[i + 148*t];
R[i + 149*t] = A[i + 149*t];
R[i + 150*t] = A[i + 150*t];
R[i + 151*t] = A[i + 151*t];
R[i + 152*t] = A[i + 152*t];
R[i + 153*t] = A[i + 153*t];
R[i + 154*t] = A[i + 154*t];
R[i + 155*t] = A[i + 155*t];
R[i + 156*t] = A[i + 156*t];
R[i + 157*t] = A[i + 157*t];
R[i + 158*t] = A[i + 158*t];
R[i + 159*t] = A[i + 159*t];
R[i + 160*t] = A[i + 160*t];
R[i + 161*t] = A[i + 161*t];
R[i + 162*t] = A[i + 162*t];
R[i + 163*t] = A[i + 163*t];
R[i + 164*t] = A[i + 164*t];
R[i + 165*t] = A[i + 165*t];
R[i + 166*t] = A[i + 166*t];
R[i + 167*t] = A[i + 167*t];
R[i + 168*t] = A[i + 168*t];
R[i + 169*t] = A[i + 169*t];
R[i + 170*t] = A[i + 170*t];
R[i + 171*t] = A[i + 171*t];
R[i + 172*t] = A[i + 172*t];
R[i + 173*t] = A[i + 173*t];
R[i + 174*t] = A[i + 174*t];
R[i + 175*t] = A[i + 175*t];
R[i + 176*t] = A[i + 176*t];
R[i + 177*t] = A[i + 177*t];
R[i + 178*t] = A[i + 178*t];
R[i + 179*t] = A[i + 179*t];
R[i + 180*t] = A[i + 180*t];
R[i + 181*t] = A[i + 181*t];
R[i + 182*t] = A[i + 182*t];
R[i + 183*t] = A[i + 183*t];
R[i + 184*t] = A[i + 184*t];
R[i + 185*t] = A[i + 185*t];
R[i + 186*t] = A[i + 186*t];
R[i + 187*t] = A[i + 187*t];
R[i + 188*t] = A[i + 188*t];
R[i + 189*t] = A[i + 189*t];
R[i + 190*t] = A[i + 190*t];
R[i + 191*t] = A[i + 191*t];
R[i + 192*t] = A[i + 192*t];
R[i + 193*t] = A[i + 193*t];
R[i + 194*t] = A[i + 194*t];
R[i + 195*t] = A[i + 195*t];
R[i + 196*t] = A[i + 196*t];
R[i + 197*t] = A[i + 197*t];
R[i + 198*t] = A[i + 198*t];
R[i + 199*t] = A[i + 199*t];
R[i + 200*t] = A[i + 200*t];
R[i + 201*t] = A[i + 201*t];
R[i + 202*t] = A[i + 202*t];
R[i + 203*t] = A[i + 203*t];
R[i + 204*t] = A[i + 204*t];
R[i + 205*t] = A[i + 205*t];
__syncthreads();
for (int iter=0; iter< n_iter; iter++) {
R[i + 206*t] = Op[i + 0*t] ? R[B[i + 0*t]] * R[C[i + 0*t]] : R[B[i + 0*t]] + R[C[i + 0*t]];
R[i + 207*t] = Op[i + 1*t] ? R[B[i + 1*t]] * R[C[i + 1*t]] : R[B[i + 1*t]] + R[C[i + 1*t]];
R[i + 208*t] = Op[i + 2*t] ? R[B[i + 2*t]] * R[C[i + 2*t]] : R[B[i + 2*t]] + R[C[i + 2*t]];
R[i + 209*t] = Op[i + 3*t] ? R[B[i + 3*t]] * R[C[i + 3*t]] : R[B[i + 3*t]] + R[C[i + 3*t]];
R[i + 210*t] = Op[i + 4*t] ? R[B[i + 4*t]] * R[C[i + 4*t]] : R[B[i + 4*t]] + R[C[i + 4*t]];
R[i + 211*t] = Op[i + 5*t] ? R[B[i + 5*t]] * R[C[i + 5*t]] : R[B[i + 5*t]] + R[C[i + 5*t]];
R[i + 212*t] = Op[i + 6*t] ? R[B[i + 6*t]] * R[C[i + 6*t]] : R[B[i + 6*t]] + R[C[i + 6*t]];
R[i + 213*t] = Op[i + 7*t] ? R[B[i + 7*t]] * R[C[i + 7*t]] : R[B[i + 7*t]] + R[C[i + 7*t]];
R[i + 214*t] = Op[i + 8*t] ? R[B[i + 8*t]] * R[C[i + 8*t]] : R[B[i + 8*t]] + R[C[i + 8*t]];
R[i + 215*t] = Op[i + 9*t] ? R[B[i + 9*t]] * R[C[i + 9*t]] : R[B[i + 9*t]] + R[C[i + 9*t]];
R[i + 216*t] = Op[i + 10*t] ? R[B[i + 10*t]] * R[C[i + 10*t]] : R[B[i + 10*t]] + R[C[i + 10*t]];
R[i + 217*t] = Op[i + 11*t] ? R[B[i + 11*t]] * R[C[i + 11*t]] : R[B[i + 11*t]] + R[C[i + 11*t]];
R[i + 218*t] = Op[i + 12*t] ? R[B[i + 12*t]] * R[C[i + 12*t]] : R[B[i + 12*t]] + R[C[i + 12*t]];
R[i + 219*t] = Op[i + 13*t] ? R[B[i + 13*t]] * R[C[i + 13*t]] : R[B[i + 13*t]] + R[C[i + 13*t]];
R[i + 220*t] = Op[i + 14*t] ? R[B[i + 14*t]] * R[C[i + 14*t]] : R[B[i + 14*t]] + R[C[i + 14*t]];
R[i + 221*t] = Op[i + 15*t] ? R[B[i + 15*t]] * R[C[i + 15*t]] : R[B[i + 15*t]] + R[C[i + 15*t]];
R[i + 222*t] = Op[i + 16*t] ? R[B[i + 16*t]] * R[C[i + 16*t]] : R[B[i + 16*t]] + R[C[i + 16*t]];
R[i + 223*t] = Op[i + 17*t] ? R[B[i + 17*t]] * R[C[i + 17*t]] : R[B[i + 17*t]] + R[C[i + 17*t]];
R[i + 224*t] = Op[i + 18*t] ? R[B[i + 18*t]] * R[C[i + 18*t]] : R[B[i + 18*t]] + R[C[i + 18*t]];
R[i + 225*t] = Op[i + 19*t] ? R[B[i + 19*t]] * R[C[i + 19*t]] : R[B[i + 19*t]] + R[C[i + 19*t]];
R[i + 226*t] = Op[i + 20*t] ? R[B[i + 20*t]] * R[C[i + 20*t]] : R[B[i + 20*t]] + R[C[i + 20*t]];
R[i + 227*t] = Op[i + 21*t] ? R[B[i + 21*t]] * R[C[i + 21*t]] : R[B[i + 21*t]] + R[C[i + 21*t]];
R[i + 228*t] = Op[i + 22*t] ? R[B[i + 22*t]] * R[C[i + 22*t]] : R[B[i + 22*t]] + R[C[i + 22*t]];
R[i + 229*t] = Op[i + 23*t] ? R[B[i + 23*t]] * R[C[i + 23*t]] : R[B[i + 23*t]] + R[C[i + 23*t]];
R[i + 230*t] = Op[i + 24*t] ? R[B[i + 24*t]] * R[C[i + 24*t]] : R[B[i + 24*t]] + R[C[i + 24*t]];
R[i + 231*t] = Op[i + 25*t] ? R[B[i + 25*t]] * R[C[i + 25*t]] : R[B[i + 25*t]] + R[C[i + 25*t]];
R[i + 232*t] = Op[i + 26*t] ? R[B[i + 26*t]] * R[C[i + 26*t]] : R[B[i + 26*t]] + R[C[i + 26*t]];
R[i + 233*t] = Op[i + 27*t] ? R[B[i + 27*t]] * R[C[i + 27*t]] : R[B[i + 27*t]] + R[C[i + 27*t]];
R[i + 234*t] = Op[i + 28*t] ? R[B[i + 28*t]] * R[C[i + 28*t]] : R[B[i + 28*t]] + R[C[i + 28*t]];
R[i + 235*t] = Op[i + 29*t] ? R[B[i + 29*t]] * R[C[i + 29*t]] : R[B[i + 29*t]] + R[C[i + 29*t]];
R[i + 236*t] = Op[i + 30*t] ? R[B[i + 30*t]] * R[C[i + 30*t]] : R[B[i + 30*t]] + R[C[i + 30*t]];
R[i + 237*t] = Op[i + 31*t] ? R[B[i + 31*t]] * R[C[i + 31*t]] : R[B[i + 31*t]] + R[C[i + 31*t]];
R[i + 238*t] = Op[i + 32*t] ? R[B[i + 32*t]] * R[C[i + 32*t]] : R[B[i + 32*t]] + R[C[i + 32*t]];
R[i + 239*t] = Op[i + 33*t] ? R[B[i + 33*t]] * R[C[i + 33*t]] : R[B[i + 33*t]] + R[C[i + 33*t]];
R[i + 240*t] = Op[i + 34*t] ? R[B[i + 34*t]] * R[C[i + 34*t]] : R[B[i + 34*t]] + R[C[i + 34*t]];
R[i + 241*t] = Op[i + 35*t] ? R[B[i + 35*t]] * R[C[i + 35*t]] : R[B[i + 35*t]] + R[C[i + 35*t]];
R[i + 242*t] = Op[i + 36*t] ? R[B[i + 36*t]] * R[C[i + 36*t]] : R[B[i + 36*t]] + R[C[i + 36*t]];
R[i + 243*t] = Op[i + 37*t] ? R[B[i + 37*t]] * R[C[i + 37*t]] : R[B[i + 37*t]] + R[C[i + 37*t]];
R[i + 244*t] = Op[i + 38*t] ? R[B[i + 38*t]] * R[C[i + 38*t]] : R[B[i + 38*t]] + R[C[i + 38*t]];
R[i + 245*t] = Op[i + 39*t] ? R[B[i + 39*t]] * R[C[i + 39*t]] : R[B[i + 39*t]] + R[C[i + 39*t]];
R[i + 246*t] = Op[i + 40*t] ? R[B[i + 40*t]] * R[C[i + 40*t]] : R[B[i + 40*t]] + R[C[i + 40*t]];
R[i + 247*t] = Op[i + 41*t] ? R[B[i + 41*t]] * R[C[i + 41*t]] : R[B[i + 41*t]] + R[C[i + 41*t]];
R[i + 248*t] = Op[i + 42*t] ? R[B[i + 42*t]] * R[C[i + 42*t]] : R[B[i + 42*t]] + R[C[i + 42*t]];
R[i + 249*t] = Op[i + 43*t] ? R[B[i + 43*t]] * R[C[i + 43*t]] : R[B[i + 43*t]] + R[C[i + 43*t]];
__syncthreads();
R[i + 250*t] = Op[i + 44*t] ? R[B[i + 44*t]] * R[C[i + 44*t]] : R[B[i + 44*t]] + R[C[i + 44*t]];
R[i + 251*t] = Op[i + 45*t] ? R[B[i + 45*t]] * R[C[i + 45*t]] : R[B[i + 45*t]] + R[C[i + 45*t]];
R[i + 252*t] = Op[i + 46*t] ? R[B[i + 46*t]] * R[C[i + 46*t]] : R[B[i + 46*t]] + R[C[i + 46*t]];
R[i + 253*t] = Op[i + 47*t] ? R[B[i + 47*t]] * R[C[i + 47*t]] : R[B[i + 47*t]] + R[C[i + 47*t]];
R[i + 254*t] = Op[i + 48*t] ? R[B[i + 48*t]] * R[C[i + 48*t]] : R[B[i + 48*t]] + R[C[i + 48*t]];
R[i + 255*t] = Op[i + 49*t] ? R[B[i + 49*t]] * R[C[i + 49*t]] : R[B[i + 49*t]] + R[C[i + 49*t]];
R[i + 256*t] = Op[i + 50*t] ? R[B[i + 50*t]] * R[C[i + 50*t]] : R[B[i + 50*t]] + R[C[i + 50*t]];
R[i + 257*t] = Op[i + 51*t] ? R[B[i + 51*t]] * R[C[i + 51*t]] : R[B[i + 51*t]] + R[C[i + 51*t]];
R[i + 258*t] = Op[i + 52*t] ? R[B[i + 52*t]] * R[C[i + 52*t]] : R[B[i + 52*t]] + R[C[i + 52*t]];
R[i + 259*t] = Op[i + 53*t] ? R[B[i + 53*t]] * R[C[i + 53*t]] : R[B[i + 53*t]] + R[C[i + 53*t]];
R[i + 260*t] = Op[i + 54*t] ? R[B[i + 54*t]] * R[C[i + 54*t]] : R[B[i + 54*t]] + R[C[i + 54*t]];
R[i + 261*t] = Op[i + 55*t] ? R[B[i + 55*t]] * R[C[i + 55*t]] : R[B[i + 55*t]] + R[C[i + 55*t]];
R[i + 262*t] = Op[i + 56*t] ? R[B[i + 56*t]] * R[C[i + 56*t]] : R[B[i + 56*t]] + R[C[i + 56*t]];
R[i + 263*t] = Op[i + 57*t] ? R[B[i + 57*t]] * R[C[i + 57*t]] : R[B[i + 57*t]] + R[C[i + 57*t]];
R[i + 264*t] = Op[i + 58*t] ? R[B[i + 58*t]] * R[C[i + 58*t]] : R[B[i + 58*t]] + R[C[i + 58*t]];
R[i + 265*t] = Op[i + 59*t] ? R[B[i + 59*t]] * R[C[i + 59*t]] : R[B[i + 59*t]] + R[C[i + 59*t]];
R[i + 266*t] = Op[i + 60*t] ? R[B[i + 60*t]] * R[C[i + 60*t]] : R[B[i + 60*t]] + R[C[i + 60*t]];
R[i + 267*t] = Op[i + 61*t] ? R[B[i + 61*t]] * R[C[i + 61*t]] : R[B[i + 61*t]] + R[C[i + 61*t]];
R[i + 268*t] = Op[i + 62*t] ? R[B[i + 62*t]] * R[C[i + 62*t]] : R[B[i + 62*t]] + R[C[i + 62*t]];
R[i + 269*t] = Op[i + 63*t] ? R[B[i + 63*t]] * R[C[i + 63*t]] : R[B[i + 63*t]] + R[C[i + 63*t]];
R[i + 270*t] = Op[i + 64*t] ? R[B[i + 64*t]] * R[C[i + 64*t]] : R[B[i + 64*t]] + R[C[i + 64*t]];
R[i + 271*t] = Op[i + 65*t] ? R[B[i + 65*t]] * R[C[i + 65*t]] : R[B[i + 65*t]] + R[C[i + 65*t]];
R[i + 272*t] = Op[i + 66*t] ? R[B[i + 66*t]] * R[C[i + 66*t]] : R[B[i + 66*t]] + R[C[i + 66*t]];
R[i + 273*t] = Op[i + 67*t] ? R[B[i + 67*t]] * R[C[i + 67*t]] : R[B[i + 67*t]] + R[C[i + 67*t]];
R[i + 274*t] = Op[i + 68*t] ? R[B[i + 68*t]] * R[C[i + 68*t]] : R[B[i + 68*t]] + R[C[i + 68*t]];
R[i + 275*t] = Op[i + 69*t] ? R[B[i + 69*t]] * R[C[i + 69*t]] : R[B[i + 69*t]] + R[C[i + 69*t]];
R[i + 276*t] = Op[i + 70*t] ? R[B[i + 70*t]] * R[C[i + 70*t]] : R[B[i + 70*t]] + R[C[i + 70*t]];
__syncthreads();
R[i + 277*t] = Op[i + 71*t] ? R[B[i + 71*t]] * R[C[i + 71*t]] : R[B[i + 71*t]] + R[C[i + 71*t]];
R[i + 278*t] = Op[i + 72*t] ? R[B[i + 72*t]] * R[C[i + 72*t]] : R[B[i + 72*t]] + R[C[i + 72*t]];
R[i + 279*t] = Op[i + 73*t] ? R[B[i + 73*t]] * R[C[i + 73*t]] : R[B[i + 73*t]] + R[C[i + 73*t]];
R[i + 280*t] = Op[i + 74*t] ? R[B[i + 74*t]] * R[C[i + 74*t]] : R[B[i + 74*t]] + R[C[i + 74*t]];
R[i + 281*t] = Op[i + 75*t] ? R[B[i + 75*t]] * R[C[i + 75*t]] : R[B[i + 75*t]] + R[C[i + 75*t]];
R[i + 282*t] = Op[i + 76*t] ? R[B[i + 76*t]] * R[C[i + 76*t]] : R[B[i + 76*t]] + R[C[i + 76*t]];
R[i + 283*t] = Op[i + 77*t] ? R[B[i + 77*t]] * R[C[i + 77*t]] : R[B[i + 77*t]] + R[C[i + 77*t]];
R[i + 284*t] = Op[i + 78*t] ? R[B[i + 78*t]] * R[C[i + 78*t]] : R[B[i + 78*t]] + R[C[i + 78*t]];
R[i + 285*t] = Op[i + 79*t] ? R[B[i + 79*t]] * R[C[i + 79*t]] : R[B[i + 79*t]] + R[C[i + 79*t]];
R[i + 286*t] = Op[i + 80*t] ? R[B[i + 80*t]] * R[C[i + 80*t]] : R[B[i + 80*t]] + R[C[i + 80*t]];
R[i + 287*t] = Op[i + 81*t] ? R[B[i + 81*t]] * R[C[i + 81*t]] : R[B[i + 81*t]] + R[C[i + 81*t]];
R[i + 288*t] = Op[i + 82*t] ? R[B[i + 82*t]] * R[C[i + 82*t]] : R[B[i + 82*t]] + R[C[i + 82*t]];
R[i + 289*t] = Op[i + 83*t] ? R[B[i + 83*t]] * R[C[i + 83*t]] : R[B[i + 83*t]] + R[C[i + 83*t]];
R[i + 290*t] = Op[i + 84*t] ? R[B[i + 84*t]] * R[C[i + 84*t]] : R[B[i + 84*t]] + R[C[i + 84*t]];
R[i + 291*t] = Op[i + 85*t] ? R[B[i + 85*t]] * R[C[i + 85*t]] : R[B[i + 85*t]] + R[C[i + 85*t]];
R[i + 292*t] = Op[i + 86*t] ? R[B[i + 86*t]] * R[C[i + 86*t]] : R[B[i + 86*t]] + R[C[i + 86*t]];
R[i + 293*t] = Op[i + 87*t] ? R[B[i + 87*t]] * R[C[i + 87*t]] : R[B[i + 87*t]] + R[C[i + 87*t]];
R[i + 294*t] = Op[i + 88*t] ? R[B[i + 88*t]] * R[C[i + 88*t]] : R[B[i + 88*t]] + R[C[i + 88*t]];
R[i + 295*t] = Op[i + 89*t] ? R[B[i + 89*t]] * R[C[i + 89*t]] : R[B[i + 89*t]] + R[C[i + 89*t]];
R[i + 296*t] = Op[i + 90*t] ? R[B[i + 90*t]] * R[C[i + 90*t]] : R[B[i + 90*t]] + R[C[i + 90*t]];
R[i + 297*t] = Op[i + 91*t] ? R[B[i + 91*t]] * R[C[i + 91*t]] : R[B[i + 91*t]] + R[C[i + 91*t]];
R[i + 298*t] = Op[i + 92*t] ? R[B[i + 92*t]] * R[C[i + 92*t]] : R[B[i + 92*t]] + R[C[i + 92*t]];
R[i + 299*t] = Op[i + 93*t] ? R[B[i + 93*t]] * R[C[i + 93*t]] : R[B[i + 93*t]] + R[C[i + 93*t]];
R[i + 300*t] = Op[i + 94*t] ? R[B[i + 94*t]] * R[C[i + 94*t]] : R[B[i + 94*t]] + R[C[i + 94*t]];
R[i + 301*t] = Op[i + 95*t] ? R[B[i + 95*t]] * R[C[i + 95*t]] : R[B[i + 95*t]] + R[C[i + 95*t]];
R[i + 302*t] = Op[i + 96*t] ? R[B[i + 96*t]] * R[C[i + 96*t]] : R[B[i + 96*t]] + R[C[i + 96*t]];
R[i + 303*t] = Op[i + 97*t] ? R[B[i + 97*t]] * R[C[i + 97*t]] : R[B[i + 97*t]] + R[C[i + 97*t]];
R[i + 304*t] = Op[i + 98*t] ? R[B[i + 98*t]] * R[C[i + 98*t]] : R[B[i + 98*t]] + R[C[i + 98*t]];
R[i + 305*t] = Op[i + 99*t] ? R[B[i + 99*t]] * R[C[i + 99*t]] : R[B[i + 99*t]] + R[C[i + 99*t]];
R[i + 306*t] = Op[i + 100*t] ? R[B[i + 100*t]] * R[C[i + 100*t]] : R[B[i + 100*t]] + R[C[i + 100*t]];
R[i + 307*t] = Op[i + 101*t] ? R[B[i + 101*t]] * R[C[i + 101*t]] : R[B[i + 101*t]] + R[C[i + 101*t]];
R[i + 308*t] = Op[i + 102*t] ? R[B[i + 102*t]] * R[C[i + 102*t]] : R[B[i + 102*t]] + R[C[i + 102*t]];
R[i + 309*t] = Op[i + 103*t] ? R[B[i + 103*t]] * R[C[i + 103*t]] : R[B[i + 103*t]] + R[C[i + 103*t]];
R[i + 310*t] = Op[i + 104*t] ? R[B[i + 104*t]] * R[C[i + 104*t]] : R[B[i + 104*t]] + R[C[i + 104*t]];
R[i + 311*t] = Op[i + 105*t] ? R[B[i + 105*t]] * R[C[i + 105*t]] : R[B[i + 105*t]] + R[C[i + 105*t]];
R[i + 312*t] = Op[i + 106*t] ? R[B[i + 106*t]] * R[C[i + 106*t]] : R[B[i + 106*t]] + R[C[i + 106*t]];
__syncthreads();
R[i + 313*t] = Op[i + 107*t] ? R[B[i + 107*t]] * R[C[i + 107*t]] : R[B[i + 107*t]] + R[C[i + 107*t]];
R[i + 314*t] = Op[i + 108*t] ? R[B[i + 108*t]] * R[C[i + 108*t]] : R[B[i + 108*t]] + R[C[i + 108*t]];
R[i + 315*t] = Op[i + 109*t] ? R[B[i + 109*t]] * R[C[i + 109*t]] : R[B[i + 109*t]] + R[C[i + 109*t]];
R[i + 316*t] = Op[i + 110*t] ? R[B[i + 110*t]] * R[C[i + 110*t]] : R[B[i + 110*t]] + R[C[i + 110*t]];
R[i + 317*t] = Op[i + 111*t] ? R[B[i + 111*t]] * R[C[i + 111*t]] : R[B[i + 111*t]] + R[C[i + 111*t]];
R[i + 318*t] = Op[i + 112*t] ? R[B[i + 112*t]] * R[C[i + 112*t]] : R[B[i + 112*t]] + R[C[i + 112*t]];
R[i + 319*t] = Op[i + 113*t] ? R[B[i + 113*t]] * R[C[i + 113*t]] : R[B[i + 113*t]] + R[C[i + 113*t]];
R[i + 320*t] = Op[i + 114*t] ? R[B[i + 114*t]] * R[C[i + 114*t]] : R[B[i + 114*t]] + R[C[i + 114*t]];
R[i + 321*t] = Op[i + 115*t] ? R[B[i + 115*t]] * R[C[i + 115*t]] : R[B[i + 115*t]] + R[C[i + 115*t]];
R[i + 322*t] = Op[i + 116*t] ? R[B[i + 116*t]] * R[C[i + 116*t]] : R[B[i + 116*t]] + R[C[i + 116*t]];
R[i + 323*t] = Op[i + 117*t] ? R[B[i + 117*t]] * R[C[i + 117*t]] : R[B[i + 117*t]] + R[C[i + 117*t]];
R[i + 324*t] = Op[i + 118*t] ? R[B[i + 118*t]] * R[C[i + 118*t]] : R[B[i + 118*t]] + R[C[i + 118*t]];
R[i + 325*t] = Op[i + 119*t] ? R[B[i + 119*t]] * R[C[i + 119*t]] : R[B[i + 119*t]] + R[C[i + 119*t]];
R[i + 326*t] = Op[i + 120*t] ? R[B[i + 120*t]] * R[C[i + 120*t]] : R[B[i + 120*t]] + R[C[i + 120*t]];
R[i + 327*t] = Op[i + 121*t] ? R[B[i + 121*t]] * R[C[i + 121*t]] : R[B[i + 121*t]] + R[C[i + 121*t]];
R[i + 328*t] = Op[i + 122*t] ? R[B[i + 122*t]] * R[C[i + 122*t]] : R[B[i + 122*t]] + R[C[i + 122*t]];
R[i + 329*t] = Op[i + 123*t] ? R[B[i + 123*t]] * R[C[i + 123*t]] : R[B[i + 123*t]] + R[C[i + 123*t]];
R[i + 330*t] = Op[i + 124*t] ? R[B[i + 124*t]] * R[C[i + 124*t]] : R[B[i + 124*t]] + R[C[i + 124*t]];
R[i + 331*t] = Op[i + 125*t] ? R[B[i + 125*t]] * R[C[i + 125*t]] : R[B[i + 125*t]] + R[C[i + 125*t]];
R[i + 332*t] = Op[i + 126*t] ? R[B[i + 126*t]] * R[C[i + 126*t]] : R[B[i + 126*t]] + R[C[i + 126*t]];
R[i + 333*t] = Op[i + 127*t] ? R[B[i + 127*t]] * R[C[i + 127*t]] : R[B[i + 127*t]] + R[C[i + 127*t]];
R[i + 334*t] = Op[i + 128*t] ? R[B[i + 128*t]] * R[C[i + 128*t]] : R[B[i + 128*t]] + R[C[i + 128*t]];
R[i + 335*t] = Op[i + 129*t] ? R[B[i + 129*t]] * R[C[i + 129*t]] : R[B[i + 129*t]] + R[C[i + 129*t]];
R[i + 336*t] = Op[i + 130*t] ? R[B[i + 130*t]] * R[C[i + 130*t]] : R[B[i + 130*t]] + R[C[i + 130*t]];
R[i + 337*t] = Op[i + 131*t] ? R[B[i + 131*t]] * R[C[i + 131*t]] : R[B[i + 131*t]] + R[C[i + 131*t]];
R[i + 338*t] = Op[i + 132*t] ? R[B[i + 132*t]] * R[C[i + 132*t]] : R[B[i + 132*t]] + R[C[i + 132*t]];
R[i + 339*t] = Op[i + 133*t] ? R[B[i + 133*t]] * R[C[i + 133*t]] : R[B[i + 133*t]] + R[C[i + 133*t]];
R[i + 340*t] = Op[i + 134*t] ? R[B[i + 134*t]] * R[C[i + 134*t]] : R[B[i + 134*t]] + R[C[i + 134*t]];
R[i + 341*t] = Op[i + 135*t] ? R[B[i + 135*t]] * R[C[i + 135*t]] : R[B[i + 135*t]] + R[C[i + 135*t]];
R[i + 342*t] = Op[i + 136*t] ? R[B[i + 136*t]] * R[C[i + 136*t]] : R[B[i + 136*t]] + R[C[i + 136*t]];
R[i + 343*t] = Op[i + 137*t] ? R[B[i + 137*t]] * R[C[i + 137*t]] : R[B[i + 137*t]] + R[C[i + 137*t]];
R[i + 344*t] = Op[i + 138*t] ? R[B[i + 138*t]] * R[C[i + 138*t]] : R[B[i + 138*t]] + R[C[i + 138*t]];
R[i + 345*t] = Op[i + 139*t] ? R[B[i + 139*t]] * R[C[i + 139*t]] : R[B[i + 139*t]] + R[C[i + 139*t]];
R[i + 346*t] = Op[i + 140*t] ? R[B[i + 140*t]] * R[C[i + 140*t]] : R[B[i + 140*t]] + R[C[i + 140*t]];
R[i + 347*t] = Op[i + 141*t] ? R[B[i + 141*t]] * R[C[i + 141*t]] : R[B[i + 141*t]] + R[C[i + 141*t]];
__syncthreads();
R[i + 348*t] = Op[i + 142*t] ? R[B[i + 142*t]] * R[C[i + 142*t]] : R[B[i + 142*t]] + R[C[i + 142*t]];
R[i + 349*t] = Op[i + 143*t] ? R[B[i + 143*t]] * R[C[i + 143*t]] : R[B[i + 143*t]] + R[C[i + 143*t]];
R[i + 350*t] = Op[i + 144*t] ? R[B[i + 144*t]] * R[C[i + 144*t]] : R[B[i + 144*t]] + R[C[i + 144*t]];
R[i + 351*t] = Op[i + 145*t] ? R[B[i + 145*t]] * R[C[i + 145*t]] : R[B[i + 145*t]] + R[C[i + 145*t]];
R[i + 352*t] = Op[i + 146*t] ? R[B[i + 146*t]] * R[C[i + 146*t]] : R[B[i + 146*t]] + R[C[i + 146*t]];
R[i + 353*t] = Op[i + 147*t] ? R[B[i + 147*t]] * R[C[i + 147*t]] : R[B[i + 147*t]] + R[C[i + 147*t]];
R[i + 354*t] = Op[i + 148*t] ? R[B[i + 148*t]] * R[C[i + 148*t]] : R[B[i + 148*t]] + R[C[i + 148*t]];
R[i + 355*t] = Op[i + 149*t] ? R[B[i + 149*t]] * R[C[i + 149*t]] : R[B[i + 149*t]] + R[C[i + 149*t]];
R[i + 356*t] = Op[i + 150*t] ? R[B[i + 150*t]] * R[C[i + 150*t]] : R[B[i + 150*t]] + R[C[i + 150*t]];
R[i + 357*t] = Op[i + 151*t] ? R[B[i + 151*t]] * R[C[i + 151*t]] : R[B[i + 151*t]] + R[C[i + 151*t]];
R[i + 358*t] = Op[i + 152*t] ? R[B[i + 152*t]] * R[C[i + 152*t]] : R[B[i + 152*t]] + R[C[i + 152*t]];
R[i + 359*t] = Op[i + 153*t] ? R[B[i + 153*t]] * R[C[i + 153*t]] : R[B[i + 153*t]] + R[C[i + 153*t]];
R[i + 360*t] = Op[i + 154*t] ? R[B[i + 154*t]] * R[C[i + 154*t]] : R[B[i + 154*t]] + R[C[i + 154*t]];
R[i + 361*t] = Op[i + 155*t] ? R[B[i + 155*t]] * R[C[i + 155*t]] : R[B[i + 155*t]] + R[C[i + 155*t]];
R[i + 362*t] = Op[i + 156*t] ? R[B[i + 156*t]] * R[C[i + 156*t]] : R[B[i + 156*t]] + R[C[i + 156*t]];
R[i + 363*t] = Op[i + 157*t] ? R[B[i + 157*t]] * R[C[i + 157*t]] : R[B[i + 157*t]] + R[C[i + 157*t]];
R[i + 364*t] = Op[i + 158*t] ? R[B[i + 158*t]] * R[C[i + 158*t]] : R[B[i + 158*t]] + R[C[i + 158*t]];
R[i + 365*t] = Op[i + 159*t] ? R[B[i + 159*t]] * R[C[i + 159*t]] : R[B[i + 159*t]] + R[C[i + 159*t]];
R[i + 366*t] = Op[i + 160*t] ? R[B[i + 160*t]] * R[C[i + 160*t]] : R[B[i + 160*t]] + R[C[i + 160*t]];
R[i + 367*t] = Op[i + 161*t] ? R[B[i + 161*t]] * R[C[i + 161*t]] : R[B[i + 161*t]] + R[C[i + 161*t]];
R[i + 368*t] = Op[i + 162*t] ? R[B[i + 162*t]] * R[C[i + 162*t]] : R[B[i + 162*t]] + R[C[i + 162*t]];
R[i + 369*t] = Op[i + 163*t] ? R[B[i + 163*t]] * R[C[i + 163*t]] : R[B[i + 163*t]] + R[C[i + 163*t]];
R[i + 370*t] = Op[i + 164*t] ? R[B[i + 164*t]] * R[C[i + 164*t]] : R[B[i + 164*t]] + R[C[i + 164*t]];
R[i + 371*t] = Op[i + 165*t] ? R[B[i + 165*t]] * R[C[i + 165*t]] : R[B[i + 165*t]] + R[C[i + 165*t]];
R[i + 372*t] = Op[i + 166*t] ? R[B[i + 166*t]] * R[C[i + 166*t]] : R[B[i + 166*t]] + R[C[i + 166*t]];
R[i + 373*t] = Op[i + 167*t] ? R[B[i + 167*t]] * R[C[i + 167*t]] : R[B[i + 167*t]] + R[C[i + 167*t]];
R[i + 374*t] = Op[i + 168*t] ? R[B[i + 168*t]] * R[C[i + 168*t]] : R[B[i + 168*t]] + R[C[i + 168*t]];
__syncthreads();
R[i + 375*t] = Op[i + 169*t] ? R[B[i + 169*t]] * R[C[i + 169*t]] : R[B[i + 169*t]] + R[C[i + 169*t]];
R[i + 376*t] = Op[i + 170*t] ? R[B[i + 170*t]] * R[C[i + 170*t]] : R[B[i + 170*t]] + R[C[i + 170*t]];
R[i + 377*t] = Op[i + 171*t] ? R[B[i + 171*t]] * R[C[i + 171*t]] : R[B[i + 171*t]] + R[C[i + 171*t]];
R[i + 378*t] = Op[i + 172*t] ? R[B[i + 172*t]] * R[C[i + 172*t]] : R[B[i + 172*t]] + R[C[i + 172*t]];
R[i + 379*t] = Op[i + 173*t] ? R[B[i + 173*t]] * R[C[i + 173*t]] : R[B[i + 173*t]] + R[C[i + 173*t]];
R[i + 380*t] = Op[i + 174*t] ? R[B[i + 174*t]] * R[C[i + 174*t]] : R[B[i + 174*t]] + R[C[i + 174*t]];
R[i + 381*t] = Op[i + 175*t] ? R[B[i + 175*t]] * R[C[i + 175*t]] : R[B[i + 175*t]] + R[C[i + 175*t]];
R[i + 382*t] = Op[i + 176*t] ? R[B[i + 176*t]] * R[C[i + 176*t]] : R[B[i + 176*t]] + R[C[i + 176*t]];
R[i + 383*t] = Op[i + 177*t] ? R[B[i + 177*t]] * R[C[i + 177*t]] : R[B[i + 177*t]] + R[C[i + 177*t]];
R[i + 384*t] = Op[i + 178*t] ? R[B[i + 178*t]] * R[C[i + 178*t]] : R[B[i + 178*t]] + R[C[i + 178*t]];
R[i + 385*t] = Op[i + 179*t] ? R[B[i + 179*t]] * R[C[i + 179*t]] : R[B[i + 179*t]] + R[C[i + 179*t]];
R[i + 386*t] = Op[i + 180*t] ? R[B[i + 180*t]] * R[C[i + 180*t]] : R[B[i + 180*t]] + R[C[i + 180*t]];
R[i + 387*t] = Op[i + 181*t] ? R[B[i + 181*t]] * R[C[i + 181*t]] : R[B[i + 181*t]] + R[C[i + 181*t]];
R[i + 388*t] = Op[i + 182*t] ? R[B[i + 182*t]] * R[C[i + 182*t]] : R[B[i + 182*t]] + R[C[i + 182*t]];
R[i + 389*t] = Op[i + 183*t] ? R[B[i + 183*t]] * R[C[i + 183*t]] : R[B[i + 183*t]] + R[C[i + 183*t]];
R[i + 390*t] = Op[i + 184*t] ? R[B[i + 184*t]] * R[C[i + 184*t]] : R[B[i + 184*t]] + R[C[i + 184*t]];
R[i + 391*t] = Op[i + 185*t] ? R[B[i + 185*t]] * R[C[i + 185*t]] : R[B[i + 185*t]] + R[C[i + 185*t]];
R[i + 392*t] = Op[i + 186*t] ? R[B[i + 186*t]] * R[C[i + 186*t]] : R[B[i + 186*t]] + R[C[i + 186*t]];
R[i + 393*t] = Op[i + 187*t] ? R[B[i + 187*t]] * R[C[i + 187*t]] : R[B[i + 187*t]] + R[C[i + 187*t]];
R[i + 394*t] = Op[i + 188*t] ? R[B[i + 188*t]] * R[C[i + 188*t]] : R[B[i + 188*t]] + R[C[i + 188*t]];
R[i + 395*t] = Op[i + 189*t] ? R[B[i + 189*t]] * R[C[i + 189*t]] : R[B[i + 189*t]] + R[C[i + 189*t]];
R[i + 396*t] = Op[i + 190*t] ? R[B[i + 190*t]] * R[C[i + 190*t]] : R[B[i + 190*t]] + R[C[i + 190*t]];
R[i + 397*t] = Op[i + 191*t] ? R[B[i + 191*t]] * R[C[i + 191*t]] : R[B[i + 191*t]] + R[C[i + 191*t]];
R[i + 398*t] = Op[i + 192*t] ? R[B[i + 192*t]] * R[C[i + 192*t]] : R[B[i + 192*t]] + R[C[i + 192*t]];
R[i + 399*t] = Op[i + 193*t] ? R[B[i + 193*t]] * R[C[i + 193*t]] : R[B[i + 193*t]] + R[C[i + 193*t]];
R[i + 400*t] = Op[i + 194*t] ? R[B[i + 194*t]] * R[C[i + 194*t]] : R[B[i + 194*t]] + R[C[i + 194*t]];
R[i + 401*t] = Op[i + 195*t] ? R[B[i + 195*t]] * R[C[i + 195*t]] : R[B[i + 195*t]] + R[C[i + 195*t]];
R[i + 402*t] = Op[i + 196*t] ? R[B[i + 196*t]] * R[C[i + 196*t]] : R[B[i + 196*t]] + R[C[i + 196*t]];
__syncthreads();
R[i + 403*t] = Op[i + 197*t] ? R[B[i + 197*t]] * R[C[i + 197*t]] : R[B[i + 197*t]] + R[C[i + 197*t]];
R[i + 404*t] = Op[i + 198*t] ? R[B[i + 198*t]] * R[C[i + 198*t]] : R[B[i + 198*t]] + R[C[i + 198*t]];
R[i + 405*t] = Op[i + 199*t] ? R[B[i + 199*t]] * R[C[i + 199*t]] : R[B[i + 199*t]] + R[C[i + 199*t]];
R[i + 406*t] = Op[i + 200*t] ? R[B[i + 200*t]] * R[C[i + 200*t]] : R[B[i + 200*t]] + R[C[i + 200*t]];
R[i + 407*t] = Op[i + 201*t] ? R[B[i + 201*t]] * R[C[i + 201*t]] : R[B[i + 201*t]] + R[C[i + 201*t]];
R[i + 408*t] = Op[i + 202*t] ? R[B[i + 202*t]] * R[C[i + 202*t]] : R[B[i + 202*t]] + R[C[i + 202*t]];
R[i + 409*t] = Op[i + 203*t] ? R[B[i + 203*t]] * R[C[i + 203*t]] : R[B[i + 203*t]] + R[C[i + 203*t]];
R[i + 410*t] = Op[i + 204*t] ? R[B[i + 204*t]] * R[C[i + 204*t]] : R[B[i + 204*t]] + R[C[i + 204*t]];
R[i + 411*t] = Op[i + 205*t] ? R[B[i + 205*t]] * R[C[i + 205*t]] : R[B[i + 205*t]] + R[C[i + 205*t]];
R[i + 412*t] = Op[i + 206*t] ? R[B[i + 206*t]] * R[C[i + 206*t]] : R[B[i + 206*t]] + R[C[i + 206*t]];
R[i + 413*t] = Op[i + 207*t] ? R[B[i + 207*t]] * R[C[i + 207*t]] : R[B[i + 207*t]] + R[C[i + 207*t]];
R[i + 414*t] = Op[i + 208*t] ? R[B[i + 208*t]] * R[C[i + 208*t]] : R[B[i + 208*t]] + R[C[i + 208*t]];
R[i + 415*t] = Op[i + 209*t] ? R[B[i + 209*t]] * R[C[i + 209*t]] : R[B[i + 209*t]] + R[C[i + 209*t]];
R[i + 416*t] = Op[i + 210*t] ? R[B[i + 210*t]] * R[C[i + 210*t]] : R[B[i + 210*t]] + R[C[i + 210*t]];
R[i + 417*t] = Op[i + 211*t] ? R[B[i + 211*t]] * R[C[i + 211*t]] : R[B[i + 211*t]] + R[C[i + 211*t]];
R[i + 418*t] = Op[i + 212*t] ? R[B[i + 212*t]] * R[C[i + 212*t]] : R[B[i + 212*t]] + R[C[i + 212*t]];
R[i + 419*t] = Op[i + 213*t] ? R[B[i + 213*t]] * R[C[i + 213*t]] : R[B[i + 213*t]] + R[C[i + 213*t]];
R[i + 420*t] = Op[i + 214*t] ? R[B[i + 214*t]] * R[C[i + 214*t]] : R[B[i + 214*t]] + R[C[i + 214*t]];
R[i + 421*t] = Op[i + 215*t] ? R[B[i + 215*t]] * R[C[i + 215*t]] : R[B[i + 215*t]] + R[C[i + 215*t]];
R[i + 422*t] = Op[i + 216*t] ? R[B[i + 216*t]] * R[C[i + 216*t]] : R[B[i + 216*t]] + R[C[i + 216*t]];
R[i + 423*t] = Op[i + 217*t] ? R[B[i + 217*t]] * R[C[i + 217*t]] : R[B[i + 217*t]] + R[C[i + 217*t]];
R[i + 424*t] = Op[i + 218*t] ? R[B[i + 218*t]] * R[C[i + 218*t]] : R[B[i + 218*t]] + R[C[i + 218*t]];
R[i + 425*t] = Op[i + 219*t] ? R[B[i + 219*t]] * R[C[i + 219*t]] : R[B[i + 219*t]] + R[C[i + 219*t]];
R[i + 426*t] = Op[i + 220*t] ? R[B[i + 220*t]] * R[C[i + 220*t]] : R[B[i + 220*t]] + R[C[i + 220*t]];
__syncthreads();
R[i + 427*t] = Op[i + 221*t] ? R[B[i + 221*t]] * R[C[i + 221*t]] : R[B[i + 221*t]] + R[C[i + 221*t]];
R[i + 428*t] = Op[i + 222*t] ? R[B[i + 222*t]] * R[C[i + 222*t]] : R[B[i + 222*t]] + R[C[i + 222*t]];
R[i + 429*t] = Op[i + 223*t] ? R[B[i + 223*t]] * R[C[i + 223*t]] : R[B[i + 223*t]] + R[C[i + 223*t]];
R[i + 430*t] = Op[i + 224*t] ? R[B[i + 224*t]] * R[C[i + 224*t]] : R[B[i + 224*t]] + R[C[i + 224*t]];
R[i + 431*t] = Op[i + 225*t] ? R[B[i + 225*t]] * R[C[i + 225*t]] : R[B[i + 225*t]] + R[C[i + 225*t]];
R[i + 432*t] = Op[i + 226*t] ? R[B[i + 226*t]] * R[C[i + 226*t]] : R[B[i + 226*t]] + R[C[i + 226*t]];
R[i + 433*t] = Op[i + 227*t] ? R[B[i + 227*t]] * R[C[i + 227*t]] : R[B[i + 227*t]] + R[C[i + 227*t]];
R[i + 434*t] = Op[i + 228*t] ? R[B[i + 228*t]] * R[C[i + 228*t]] : R[B[i + 228*t]] + R[C[i + 228*t]];
R[i + 435*t] = Op[i + 229*t] ? R[B[i + 229*t]] * R[C[i + 229*t]] : R[B[i + 229*t]] + R[C[i + 229*t]];
R[i + 436*t] = Op[i + 230*t] ? R[B[i + 230*t]] * R[C[i + 230*t]] : R[B[i + 230*t]] + R[C[i + 230*t]];
R[i + 437*t] = Op[i + 231*t] ? R[B[i + 231*t]] * R[C[i + 231*t]] : R[B[i + 231*t]] + R[C[i + 231*t]];
R[i + 438*t] = Op[i + 232*t] ? R[B[i + 232*t]] * R[C[i + 232*t]] : R[B[i + 232*t]] + R[C[i + 232*t]];
R[i + 439*t] = Op[i + 233*t] ? R[B[i + 233*t]] * R[C[i + 233*t]] : R[B[i + 233*t]] + R[C[i + 233*t]];
R[i + 440*t] = Op[i + 234*t] ? R[B[i + 234*t]] * R[C[i + 234*t]] : R[B[i + 234*t]] + R[C[i + 234*t]];
R[i + 441*t] = Op[i + 235*t] ? R[B[i + 235*t]] * R[C[i + 235*t]] : R[B[i + 235*t]] + R[C[i + 235*t]];
R[i + 442*t] = Op[i + 236*t] ? R[B[i + 236*t]] * R[C[i + 236*t]] : R[B[i + 236*t]] + R[C[i + 236*t]];
R[i + 443*t] = Op[i + 237*t] ? R[B[i + 237*t]] * R[C[i + 237*t]] : R[B[i + 237*t]] + R[C[i + 237*t]];
R[i + 444*t] = Op[i + 238*t] ? R[B[i + 238*t]] * R[C[i + 238*t]] : R[B[i + 238*t]] + R[C[i + 238*t]];
R[i + 445*t] = Op[i + 239*t] ? R[B[i + 239*t]] * R[C[i + 239*t]] : R[B[i + 239*t]] + R[C[i + 239*t]];
R[i + 446*t] = Op[i + 240*t] ? R[B[i + 240*t]] * R[C[i + 240*t]] : R[B[i + 240*t]] + R[C[i + 240*t]];
R[i + 447*t] = Op[i + 241*t] ? R[B[i + 241*t]] * R[C[i + 241*t]] : R[B[i + 241*t]] + R[C[i + 241*t]];
R[i + 448*t] = Op[i + 242*t] ? R[B[i + 242*t]] * R[C[i + 242*t]] : R[B[i + 242*t]] + R[C[i + 242*t]];
R[i + 449*t] = Op[i + 243*t] ? R[B[i + 243*t]] * R[C[i + 243*t]] : R[B[i + 243*t]] + R[C[i + 243*t]];
R[i + 450*t] = Op[i + 244*t] ? R[B[i + 244*t]] * R[C[i + 244*t]] : R[B[i + 244*t]] + R[C[i + 244*t]];
R[i + 451*t] = Op[i + 245*t] ? R[B[i + 245*t]] * R[C[i + 245*t]] : R[B[i + 245*t]] + R[C[i + 245*t]];
__syncthreads();
R[i + 452*t] = Op[i + 246*t] ? R[B[i + 246*t]] * R[C[i + 246*t]] : R[B[i + 246*t]] + R[C[i + 246*t]];
R[i + 453*t] = Op[i + 247*t] ? R[B[i + 247*t]] * R[C[i + 247*t]] : R[B[i + 247*t]] + R[C[i + 247*t]];
R[i + 454*t] = Op[i + 248*t] ? R[B[i + 248*t]] * R[C[i + 248*t]] : R[B[i + 248*t]] + R[C[i + 248*t]];
R[i + 455*t] = Op[i + 249*t] ? R[B[i + 249*t]] * R[C[i + 249*t]] : R[B[i + 249*t]] + R[C[i + 249*t]];
R[i + 456*t] = Op[i + 250*t] ? R[B[i + 250*t]] * R[C[i + 250*t]] : R[B[i + 250*t]] + R[C[i + 250*t]];
R[i + 457*t] = Op[i + 251*t] ? R[B[i + 251*t]] * R[C[i + 251*t]] : R[B[i + 251*t]] + R[C[i + 251*t]];
R[i + 458*t] = Op[i + 252*t] ? R[B[i + 252*t]] * R[C[i + 252*t]] : R[B[i + 252*t]] + R[C[i + 252*t]];
R[i + 459*t] = Op[i + 253*t] ? R[B[i + 253*t]] * R[C[i + 253*t]] : R[B[i + 253*t]] + R[C[i + 253*t]];
R[i + 460*t] = Op[i + 254*t] ? R[B[i + 254*t]] * R[C[i + 254*t]] : R[B[i + 254*t]] + R[C[i + 254*t]];
R[i + 461*t] = Op[i + 255*t] ? R[B[i + 255*t]] * R[C[i + 255*t]] : R[B[i + 255*t]] + R[C[i + 255*t]];
R[i + 462*t] = Op[i + 256*t] ? R[B[i + 256*t]] * R[C[i + 256*t]] : R[B[i + 256*t]] + R[C[i + 256*t]];
R[i + 463*t] = Op[i + 257*t] ? R[B[i + 257*t]] * R[C[i + 257*t]] : R[B[i + 257*t]] + R[C[i + 257*t]];
R[i + 464*t] = Op[i + 258*t] ? R[B[i + 258*t]] * R[C[i + 258*t]] : R[B[i + 258*t]] + R[C[i + 258*t]];
R[i + 465*t] = Op[i + 259*t] ? R[B[i + 259*t]] * R[C[i + 259*t]] : R[B[i + 259*t]] + R[C[i + 259*t]];
R[i + 466*t] = Op[i + 260*t] ? R[B[i + 260*t]] * R[C[i + 260*t]] : R[B[i + 260*t]] + R[C[i + 260*t]];
R[i + 467*t] = Op[i + 261*t] ? R[B[i + 261*t]] * R[C[i + 261*t]] : R[B[i + 261*t]] + R[C[i + 261*t]];
R[i + 468*t] = Op[i + 262*t] ? R[B[i + 262*t]] * R[C[i + 262*t]] : R[B[i + 262*t]] + R[C[i + 262*t]];
R[i + 469*t] = Op[i + 263*t] ? R[B[i + 263*t]] * R[C[i + 263*t]] : R[B[i + 263*t]] + R[C[i + 263*t]];
R[i + 470*t] = Op[i + 264*t] ? R[B[i + 264*t]] * R[C[i + 264*t]] : R[B[i + 264*t]] + R[C[i + 264*t]];
R[i + 471*t] = Op[i + 265*t] ? R[B[i + 265*t]] * R[C[i + 265*t]] : R[B[i + 265*t]] + R[C[i + 265*t]];
__syncthreads();
R[i + 472*t] = Op[i + 266*t] ? R[B[i + 266*t]] * R[C[i + 266*t]] : R[B[i + 266*t]] + R[C[i + 266*t]];
R[i + 473*t] = Op[i + 267*t] ? R[B[i + 267*t]] * R[C[i + 267*t]] : R[B[i + 267*t]] + R[C[i + 267*t]];
R[i + 474*t] = Op[i + 268*t] ? R[B[i + 268*t]] * R[C[i + 268*t]] : R[B[i + 268*t]] + R[C[i + 268*t]];
R[i + 475*t] = Op[i + 269*t] ? R[B[i + 269*t]] * R[C[i + 269*t]] : R[B[i + 269*t]] + R[C[i + 269*t]];
R[i + 476*t] = Op[i + 270*t] ? R[B[i + 270*t]] * R[C[i + 270*t]] : R[B[i + 270*t]] + R[C[i + 270*t]];
R[i + 477*t] = Op[i + 271*t] ? R[B[i + 271*t]] * R[C[i + 271*t]] : R[B[i + 271*t]] + R[C[i + 271*t]];
R[i + 478*t] = Op[i + 272*t] ? R[B[i + 272*t]] * R[C[i + 272*t]] : R[B[i + 272*t]] + R[C[i + 272*t]];
R[i + 479*t] = Op[i + 273*t] ? R[B[i + 273*t]] * R[C[i + 273*t]] : R[B[i + 273*t]] + R[C[i + 273*t]];
R[i + 480*t] = Op[i + 274*t] ? R[B[i + 274*t]] * R[C[i + 274*t]] : R[B[i + 274*t]] + R[C[i + 274*t]];
R[i + 481*t] = Op[i + 275*t] ? R[B[i + 275*t]] * R[C[i + 275*t]] : R[B[i + 275*t]] + R[C[i + 275*t]];
R[i + 482*t] = Op[i + 276*t] ? R[B[i + 276*t]] * R[C[i + 276*t]] : R[B[i + 276*t]] + R[C[i + 276*t]];
R[i + 483*t] = Op[i + 277*t] ? R[B[i + 277*t]] * R[C[i + 277*t]] : R[B[i + 277*t]] + R[C[i + 277*t]];
R[i + 484*t] = Op[i + 278*t] ? R[B[i + 278*t]] * R[C[i + 278*t]] : R[B[i + 278*t]] + R[C[i + 278*t]];
R[i + 485*t] = Op[i + 279*t] ? R[B[i + 279*t]] * R[C[i + 279*t]] : R[B[i + 279*t]] + R[C[i + 279*t]];
R[i + 486*t] = Op[i + 280*t] ? R[B[i + 280*t]] * R[C[i + 280*t]] : R[B[i + 280*t]] + R[C[i + 280*t]];
R[i + 487*t] = Op[i + 281*t] ? R[B[i + 281*t]] * R[C[i + 281*t]] : R[B[i + 281*t]] + R[C[i + 281*t]];
R[i + 488*t] = Op[i + 282*t] ? R[B[i + 282*t]] * R[C[i + 282*t]] : R[B[i + 282*t]] + R[C[i + 282*t]];
R[i + 489*t] = Op[i + 283*t] ? R[B[i + 283*t]] * R[C[i + 283*t]] : R[B[i + 283*t]] + R[C[i + 283*t]];
R[i + 490*t] = Op[i + 284*t] ? R[B[i + 284*t]] * R[C[i + 284*t]] : R[B[i + 284*t]] + R[C[i + 284*t]];
R[i + 491*t] = Op[i + 285*t] ? R[B[i + 285*t]] * R[C[i + 285*t]] : R[B[i + 285*t]] + R[C[i + 285*t]];
R[i + 492*t] = Op[i + 286*t] ? R[B[i + 286*t]] * R[C[i + 286*t]] : R[B[i + 286*t]] + R[C[i + 286*t]];
__syncthreads();
R[i + 493*t] = Op[i + 287*t] ? R[B[i + 287*t]] * R[C[i + 287*t]] : R[B[i + 287*t]] + R[C[i + 287*t]];
R[i + 494*t] = Op[i + 288*t] ? R[B[i + 288*t]] * R[C[i + 288*t]] : R[B[i + 288*t]] + R[C[i + 288*t]];
R[i + 495*t] = Op[i + 289*t] ? R[B[i + 289*t]] * R[C[i + 289*t]] : R[B[i + 289*t]] + R[C[i + 289*t]];
R[i + 496*t] = Op[i + 290*t] ? R[B[i + 290*t]] * R[C[i + 290*t]] : R[B[i + 290*t]] + R[C[i + 290*t]];
R[i + 497*t] = Op[i + 291*t] ? R[B[i + 291*t]] * R[C[i + 291*t]] : R[B[i + 291*t]] + R[C[i + 291*t]];
R[i + 498*t] = Op[i + 292*t] ? R[B[i + 292*t]] * R[C[i + 292*t]] : R[B[i + 292*t]] + R[C[i + 292*t]];
R[i + 499*t] = Op[i + 293*t] ? R[B[i + 293*t]] * R[C[i + 293*t]] : R[B[i + 293*t]] + R[C[i + 293*t]];
R[i + 500*t] = Op[i + 294*t] ? R[B[i + 294*t]] * R[C[i + 294*t]] : R[B[i + 294*t]] + R[C[i + 294*t]];
R[i + 501*t] = Op[i + 295*t] ? R[B[i + 295*t]] * R[C[i + 295*t]] : R[B[i + 295*t]] + R[C[i + 295*t]];
R[i + 502*t] = Op[i + 296*t] ? R[B[i + 296*t]] * R[C[i + 296*t]] : R[B[i + 296*t]] + R[C[i + 296*t]];
R[i + 503*t] = Op[i + 297*t] ? R[B[i + 297*t]] * R[C[i + 297*t]] : R[B[i + 297*t]] + R[C[i + 297*t]];
R[i + 504*t] = Op[i + 298*t] ? R[B[i + 298*t]] * R[C[i + 298*t]] : R[B[i + 298*t]] + R[C[i + 298*t]];
R[i + 505*t] = Op[i + 299*t] ? R[B[i + 299*t]] * R[C[i + 299*t]] : R[B[i + 299*t]] + R[C[i + 299*t]];
R[i + 506*t] = Op[i + 300*t] ? R[B[i + 300*t]] * R[C[i + 300*t]] : R[B[i + 300*t]] + R[C[i + 300*t]];
R[i + 507*t] = Op[i + 301*t] ? R[B[i + 301*t]] * R[C[i + 301*t]] : R[B[i + 301*t]] + R[C[i + 301*t]];
R[i + 508*t] = Op[i + 302*t] ? R[B[i + 302*t]] * R[C[i + 302*t]] : R[B[i + 302*t]] + R[C[i + 302*t]];
R[i + 509*t] = Op[i + 303*t] ? R[B[i + 303*t]] * R[C[i + 303*t]] : R[B[i + 303*t]] + R[C[i + 303*t]];
__syncthreads();
R[i + 510*t] = Op[i + 304*t] ? R[B[i + 304*t]] * R[C[i + 304*t]] : R[B[i + 304*t]] + R[C[i + 304*t]];
R[i + 511*t] = Op[i + 305*t] ? R[B[i + 305*t]] * R[C[i + 305*t]] : R[B[i + 305*t]] + R[C[i + 305*t]];
R[i + 512*t] = Op[i + 306*t] ? R[B[i + 306*t]] * R[C[i + 306*t]] : R[B[i + 306*t]] + R[C[i + 306*t]];
R[i + 513*t] = Op[i + 307*t] ? R[B[i + 307*t]] * R[C[i + 307*t]] : R[B[i + 307*t]] + R[C[i + 307*t]];
R[i + 514*t] = Op[i + 308*t] ? R[B[i + 308*t]] * R[C[i + 308*t]] : R[B[i + 308*t]] + R[C[i + 308*t]];
R[i + 515*t] = Op[i + 309*t] ? R[B[i + 309*t]] * R[C[i + 309*t]] : R[B[i + 309*t]] + R[C[i + 309*t]];
R[i + 516*t] = Op[i + 310*t] ? R[B[i + 310*t]] * R[C[i + 310*t]] : R[B[i + 310*t]] + R[C[i + 310*t]];
R[i + 517*t] = Op[i + 311*t] ? R[B[i + 311*t]] * R[C[i + 311*t]] : R[B[i + 311*t]] + R[C[i + 311*t]];
R[i + 518*t] = Op[i + 312*t] ? R[B[i + 312*t]] * R[C[i + 312*t]] : R[B[i + 312*t]] + R[C[i + 312*t]];
R[i + 519*t] = Op[i + 313*t] ? R[B[i + 313*t]] * R[C[i + 313*t]] : R[B[i + 313*t]] + R[C[i + 313*t]];
R[i + 520*t] = Op[i + 314*t] ? R[B[i + 314*t]] * R[C[i + 314*t]] : R[B[i + 314*t]] + R[C[i + 314*t]];
R[i + 521*t] = Op[i + 315*t] ? R[B[i + 315*t]] * R[C[i + 315*t]] : R[B[i + 315*t]] + R[C[i + 315*t]];
R[i + 522*t] = Op[i + 316*t] ? R[B[i + 316*t]] * R[C[i + 316*t]] : R[B[i + 316*t]] + R[C[i + 316*t]];
R[i + 523*t] = Op[i + 317*t] ? R[B[i + 317*t]] * R[C[i + 317*t]] : R[B[i + 317*t]] + R[C[i + 317*t]];
__syncthreads();
R[i + 524*t] = Op[i + 318*t] ? R[B[i + 318*t]] * R[C[i + 318*t]] : R[B[i + 318*t]] + R[C[i + 318*t]];
R[i + 525*t] = Op[i + 319*t] ? R[B[i + 319*t]] * R[C[i + 319*t]] : R[B[i + 319*t]] + R[C[i + 319*t]];
R[i + 526*t] = Op[i + 320*t] ? R[B[i + 320*t]] * R[C[i + 320*t]] : R[B[i + 320*t]] + R[C[i + 320*t]];
R[i + 527*t] = Op[i + 321*t] ? R[B[i + 321*t]] * R[C[i + 321*t]] : R[B[i + 321*t]] + R[C[i + 321*t]];
R[i + 528*t] = Op[i + 322*t] ? R[B[i + 322*t]] * R[C[i + 322*t]] : R[B[i + 322*t]] + R[C[i + 322*t]];
R[i + 529*t] = Op[i + 323*t] ? R[B[i + 323*t]] * R[C[i + 323*t]] : R[B[i + 323*t]] + R[C[i + 323*t]];
R[i + 530*t] = Op[i + 324*t] ? R[B[i + 324*t]] * R[C[i + 324*t]] : R[B[i + 324*t]] + R[C[i + 324*t]];
R[i + 531*t] = Op[i + 325*t] ? R[B[i + 325*t]] * R[C[i + 325*t]] : R[B[i + 325*t]] + R[C[i + 325*t]];
R[i + 532*t] = Op[i + 326*t] ? R[B[i + 326*t]] * R[C[i + 326*t]] : R[B[i + 326*t]] + R[C[i + 326*t]];
R[i + 533*t] = Op[i + 327*t] ? R[B[i + 327*t]] * R[C[i + 327*t]] : R[B[i + 327*t]] + R[C[i + 327*t]];
R[i + 534*t] = Op[i + 328*t] ? R[B[i + 328*t]] * R[C[i + 328*t]] : R[B[i + 328*t]] + R[C[i + 328*t]];
R[i + 535*t] = Op[i + 329*t] ? R[B[i + 329*t]] * R[C[i + 329*t]] : R[B[i + 329*t]] + R[C[i + 329*t]];
R[i + 536*t] = Op[i + 330*t] ? R[B[i + 330*t]] * R[C[i + 330*t]] : R[B[i + 330*t]] + R[C[i + 330*t]];
R[i + 537*t] = Op[i + 331*t] ? R[B[i + 331*t]] * R[C[i + 331*t]] : R[B[i + 331*t]] + R[C[i + 331*t]];
R[i + 538*t] = Op[i + 332*t] ? R[B[i + 332*t]] * R[C[i + 332*t]] : R[B[i + 332*t]] + R[C[i + 332*t]];
__syncthreads();
R[i + 539*t] = Op[i + 333*t] ? R[B[i + 333*t]] * R[C[i + 333*t]] : R[B[i + 333*t]] + R[C[i + 333*t]];
R[i + 540*t] = Op[i + 334*t] ? R[B[i + 334*t]] * R[C[i + 334*t]] : R[B[i + 334*t]] + R[C[i + 334*t]];
R[i + 541*t] = Op[i + 335*t] ? R[B[i + 335*t]] * R[C[i + 335*t]] : R[B[i + 335*t]] + R[C[i + 335*t]];
R[i + 542*t] = Op[i + 336*t] ? R[B[i + 336*t]] * R[C[i + 336*t]] : R[B[i + 336*t]] + R[C[i + 336*t]];
R[i + 543*t] = Op[i + 337*t] ? R[B[i + 337*t]] * R[C[i + 337*t]] : R[B[i + 337*t]] + R[C[i + 337*t]];
R[i + 544*t] = Op[i + 338*t] ? R[B[i + 338*t]] * R[C[i + 338*t]] : R[B[i + 338*t]] + R[C[i + 338*t]];
R[i + 545*t] = Op[i + 339*t] ? R[B[i + 339*t]] * R[C[i + 339*t]] : R[B[i + 339*t]] + R[C[i + 339*t]];
R[i + 546*t] = Op[i + 340*t] ? R[B[i + 340*t]] * R[C[i + 340*t]] : R[B[i + 340*t]] + R[C[i + 340*t]];
R[i + 547*t] = Op[i + 341*t] ? R[B[i + 341*t]] * R[C[i + 341*t]] : R[B[i + 341*t]] + R[C[i + 341*t]];
R[i + 548*t] = Op[i + 342*t] ? R[B[i + 342*t]] * R[C[i + 342*t]] : R[B[i + 342*t]] + R[C[i + 342*t]];
R[i + 549*t] = Op[i + 343*t] ? R[B[i + 343*t]] * R[C[i + 343*t]] : R[B[i + 343*t]] + R[C[i + 343*t]];
R[i + 550*t] = Op[i + 344*t] ? R[B[i + 344*t]] * R[C[i + 344*t]] : R[B[i + 344*t]] + R[C[i + 344*t]];
R[i + 551*t] = Op[i + 345*t] ? R[B[i + 345*t]] * R[C[i + 345*t]] : R[B[i + 345*t]] + R[C[i + 345*t]];
__syncthreads();
R[i + 552*t] = Op[i + 346*t] ? R[B[i + 346*t]] * R[C[i + 346*t]] : R[B[i + 346*t]] + R[C[i + 346*t]];
R[i + 553*t] = Op[i + 347*t] ? R[B[i + 347*t]] * R[C[i + 347*t]] : R[B[i + 347*t]] + R[C[i + 347*t]];
R[i + 554*t] = Op[i + 348*t] ? R[B[i + 348*t]] * R[C[i + 348*t]] : R[B[i + 348*t]] + R[C[i + 348*t]];
R[i + 555*t] = Op[i + 349*t] ? R[B[i + 349*t]] * R[C[i + 349*t]] : R[B[i + 349*t]] + R[C[i + 349*t]];
R[i + 556*t] = Op[i + 350*t] ? R[B[i + 350*t]] * R[C[i + 350*t]] : R[B[i + 350*t]] + R[C[i + 350*t]];
R[i + 557*t] = Op[i + 351*t] ? R[B[i + 351*t]] * R[C[i + 351*t]] : R[B[i + 351*t]] + R[C[i + 351*t]];
R[i + 558*t] = Op[i + 352*t] ? R[B[i + 352*t]] * R[C[i + 352*t]] : R[B[i + 352*t]] + R[C[i + 352*t]];
R[i + 559*t] = Op[i + 353*t] ? R[B[i + 353*t]] * R[C[i + 353*t]] : R[B[i + 353*t]] + R[C[i + 353*t]];
R[i + 560*t] = Op[i + 354*t] ? R[B[i + 354*t]] * R[C[i + 354*t]] : R[B[i + 354*t]] + R[C[i + 354*t]];
R[i + 561*t] = Op[i + 355*t] ? R[B[i + 355*t]] * R[C[i + 355*t]] : R[B[i + 355*t]] + R[C[i + 355*t]];
__syncthreads();
R[i + 562*t] = Op[i + 356*t] ? R[B[i + 356*t]] * R[C[i + 356*t]] : R[B[i + 356*t]] + R[C[i + 356*t]];
R[i + 563*t] = Op[i + 357*t] ? R[B[i + 357*t]] * R[C[i + 357*t]] : R[B[i + 357*t]] + R[C[i + 357*t]];
R[i + 564*t] = Op[i + 358*t] ? R[B[i + 358*t]] * R[C[i + 358*t]] : R[B[i + 358*t]] + R[C[i + 358*t]];
R[i + 565*t] = Op[i + 359*t] ? R[B[i + 359*t]] * R[C[i + 359*t]] : R[B[i + 359*t]] + R[C[i + 359*t]];
R[i + 566*t] = Op[i + 360*t] ? R[B[i + 360*t]] * R[C[i + 360*t]] : R[B[i + 360*t]] + R[C[i + 360*t]];
R[i + 567*t] = Op[i + 361*t] ? R[B[i + 361*t]] * R[C[i + 361*t]] : R[B[i + 361*t]] + R[C[i + 361*t]];
R[i + 568*t] = Op[i + 362*t] ? R[B[i + 362*t]] * R[C[i + 362*t]] : R[B[i + 362*t]] + R[C[i + 362*t]];
__syncthreads();
R[i + 569*t] = Op[i + 363*t] ? R[B[i + 363*t]] * R[C[i + 363*t]] : R[B[i + 363*t]] + R[C[i + 363*t]];
R[i + 570*t] = Op[i + 364*t] ? R[B[i + 364*t]] * R[C[i + 364*t]] : R[B[i + 364*t]] + R[C[i + 364*t]];
R[i + 571*t] = Op[i + 365*t] ? R[B[i + 365*t]] * R[C[i + 365*t]] : R[B[i + 365*t]] + R[C[i + 365*t]];
R[i + 572*t] = Op[i + 366*t] ? R[B[i + 366*t]] * R[C[i + 366*t]] : R[B[i + 366*t]] + R[C[i + 366*t]];
__syncthreads();
R[i + 573*t] = Op[i + 367*t] ? R[B[i + 367*t]] * R[C[i + 367*t]] : R[B[i + 367*t]] + R[C[i + 367*t]];
R[i + 574*t] = Op[i + 368*t] ? R[B[i + 368*t]] * R[C[i + 368*t]] : R[B[i + 368*t]] + R[C[i + 368*t]];
R[i + 575*t] = Op[i + 369*t] ? R[B[i + 369*t]] * R[C[i + 369*t]] : R[B[i + 369*t]] + R[C[i + 369*t]];
__syncthreads();
R[i + 576*t] = Op[i + 370*t] ? R[B[i + 370*t]] * R[C[i + 370*t]] : R[B[i + 370*t]] + R[C[i + 370*t]];
R[i + 577*t] = Op[i + 371*t] ? R[B[i + 371*t]] * R[C[i + 371*t]] : R[B[i + 371*t]] + R[C[i + 371*t]];
R[i + 578*t] = Op[i + 372*t] ? R[B[i + 372*t]] * R[C[i + 372*t]] : R[B[i + 372*t]] + R[C[i + 372*t]];
__syncthreads();
R[i + 579*t] = Op[i + 373*t] ? R[B[i + 373*t]] * R[C[i + 373*t]] : R[B[i + 373*t]] + R[C[i + 373*t]];
R[i + 580*t] = Op[i + 374*t] ? R[B[i + 374*t]] * R[C[i + 374*t]] : R[B[i + 374*t]] + R[C[i + 374*t]];
__syncthreads();
R[i + 581*t] = Op[i + 375*t] ? R[B[i + 375*t]] * R[C[i + 375*t]] : R[B[i + 375*t]] + R[C[i + 375*t]];
R[i + 582*t] = Op[i + 376*t] ? R[B[i + 376*t]] * R[C[i + 376*t]] : R[B[i + 376*t]] + R[C[i + 376*t]];
__syncthreads();
R[i + 583*t] = Op[i + 377*t] ? R[B[i + 377*t]] * R[C[i + 377*t]] : R[B[i + 377*t]] + R[C[i + 377*t]];
R[i + 584*t] = Op[i + 378*t] ? R[B[i + 378*t]] * R[C[i + 378*t]] : R[B[i + 378*t]] + R[C[i + 378*t]];
__syncthreads();
R[i + 585*t] = Op[i + 379*t] ? R[B[i + 379*t]] * R[C[i + 379*t]] : R[B[i + 379*t]] + R[C[i + 379*t]];
__syncthreads();
R[i + 586*t] = Op[i + 380*t] ? R[B[i + 380*t]] * R[C[i + 380*t]] : R[B[i + 380*t]] + R[C[i + 380*t]];
__syncthreads();
R[i + 587*t] = Op[i + 381*t] ? R[B[i + 381*t]] * R[C[i + 381*t]] : R[B[i + 381*t]] + R[C[i + 381*t]];
__syncthreads();
R[i + 588*t] = Op[i + 382*t] ? R[B[i + 382*t]] * R[C[i + 382*t]] : R[B[i + 382*t]] + R[C[i + 382*t]];
__syncthreads();
R[i + 589*t] = Op[i + 383*t] ? R[B[i + 383*t]] * R[C[i + 383*t]] : R[B[i + 383*t]] + R[C[i + 383*t]];
__syncthreads();
R[i + 590*t] = Op[i + 384*t] ? R[B[i + 384*t]] * R[C[i + 384*t]] : R[B[i + 384*t]] + R[C[i + 384*t]];
__syncthreads();
R[i + 591*t] = Op[i + 385*t] ? R[B[i + 385*t]] * R[C[i + 385*t]] : R[B[i + 385*t]] + R[C[i + 385*t]];
__syncthreads();
R[i + 592*t] = Op[i + 386*t] ? R[B[i + 386*t]] * R[C[i + 386*t]] : R[B[i + 386*t]] + R[C[i + 386*t]];
__syncthreads();
R[i + 593*t] = Op[i + 387*t] ? R[B[i + 387*t]] * R[C[i + 387*t]] : R[B[i + 387*t]] + R[C[i + 387*t]];
__syncthreads();
R[i + 594*t] = Op[i + 388*t] ? R[B[i + 388*t]] * R[C[i + 388*t]] : R[B[i + 388*t]] + R[C[i + 388*t]];
__syncthreads();
R[i + 595*t] = Op[i + 389*t] ? R[B[i + 389*t]] * R[C[i + 389*t]] : R[B[i + 389*t]] + R[C[i + 389*t]];
if (i==0) { final += R[595*t]; }
__syncthreads();
}
if (i==0) { A[0]= final;}
}
|
18,709 | extern "C" __global__ void scale(double* vector, double alpha, unsigned int size) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if(idx < size) {
vector[idx] *= alpha;
}
}
extern "C" __global__ void acc(double* x, double* y, double alpha, int size) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if(idx < size) {
x[idx] += y[idx] * alpha;
}
}
extern "C" __global__ void dnrm2(double* vector, double* result, int size) {
__shared__ double sdata[256];
int tid = threadIdx.x;
int idx = blockIdx.x * blockDim.x * 2 + threadIdx.x;
double value;
sdata[tid] = 0.0;
if(idx < size) {
value = vector[idx];
sdata[tid] += value * value;
}
if(idx + blockDim.x < size) {
value = vector[idx + blockDim.x];
sdata[tid] += value * value;
}
__syncthreads();
for(int s = blockDim.x / 2; s > 0; s >>= 1) {
if(tid < s) {
sdata[tid] += sdata[tid + s];
}
__syncthreads();
}
if(tid == 0) {
result[blockIdx.x] = sdata[0];
}
}
extern "C" __global__ void innerprod(double* x, double* y, double* result, int size) {
__shared__ double sdata[256];
int tid = threadIdx.x;
int idx = blockIdx.x * blockDim.x * 2 + threadIdx.x;
sdata[tid] = 0.0;
if(idx < size) {
sdata[tid] += x[idx] * y[idx];
}
if(idx + blockDim.x < size) {
sdata[tid] += x[idx + blockDim.x] * y[idx + blockDim.x];
}
__syncthreads();
for(int s = blockDim.x / 2; s > 0; s >>= 1) {
if(tid < s) {
sdata[tid] += sdata[tid + s];
}
__syncthreads();
}
if(tid == 0) {
result[blockIdx.x] = sdata[0];
}
}
|
18,710 | #include <cstdio>
#include <cuda_runtime.h>
#include "kosaraju.cuh"
/* Fill out the adjacency list and the reverse adjacency list as according to
* the routes given. Each route represents a directed edge.
*/
__global__
void cudaAirportAdjacencyKernel(int *dev_routes,
int *dev_adj,
int *dev_radj,
int n_ports,
int n_routes) {
unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
while (i < n_routes) {
int first = dev_routes[2 * i];
int second = dev_routes[2 * i + 1];
dev_adj[first * n_ports + second] = 1;
dev_radj[second * n_ports + first] = 1;
i += blockDim.x * gridDim.x;
}
}
/* Wrapper function to call cudaAirportAdjacencyKernel. */
void cudaCallAirportAdjacencyKernel(const unsigned int blocks,
const unsigned int threadsPerBlock,
int *dev_routes,
int *dev_adj,
int *dev_radj,
int n_ports,
int n_routes) {
cudaAirportAdjacencyKernel<<<blocks, threadsPerBlock>>>
(dev_routes, dev_adj, dev_radj, n_ports, n_routes);
}
/* Remove any vertices with in-degree and out-degree 0, just for optimization. */
__global__
void cudaTrimGraph(int *m,
int *row_sum,
bool *mark,
int n_ports) {
// For i = 0 to n_ports - 1 inclusive, achieve the following:
// row_sum[i] = sum from j = 0 to n_ports - 1 of m[i * n_ports + j] * !mark[j]
unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
while (i < n_ports) {
int total = 0;
for (int j = 0; j < n_ports; j++) {
total += m[i * n_ports + j] * !(mark[j]);
}
row_sum[i] = total;
i += blockDim.x * gridDim.x;
}
}
void cudaCallTrimGraph(const unsigned int blocks,
const unsigned int threadsPerBlock,
int *adj,
int *row_sum,
bool *mark,
int n_ports) {
cudaTrimGraph<<<blocks, threadsPerBlock>>>(adj, row_sum, mark, n_ports);
}
__global__
void cudaBFSKernel(int *adj,
bool *frontier,
bool *visited,
int n_ports) {
// Do the BFS search
unsigned int tid = blockIdx.x * blockDim.x + threadIdx.x;
while (tid < n_ports) {
if (frontier[tid]) {
frontier[tid] = false;
visited[tid] = true;
for (int i = 0; i < n_ports; i++) {
if (adj[tid * n_ports + i] && !visited[i]) {
frontier[i] = true;
}
}
}
tid += blockDim.x * gridDim.x;
}
}
/* Returns whether the frontier array contains any true values. */
__global__
void cudaContainsTrueKernel(bool *frontier,
int *dev_flag,
int n_ports) {
unsigned int tid = blockIdx.x * blockDim.x + threadIdx.x;
while (tid < n_ports) {
if (frontier[tid]) {
dev_flag[0] *= 0;
}
tid += blockDim.x * gridDim.x;
}
}
/* Wrapper function to perform BFS. */
void cudaCallBFSKernel(const unsigned int blocks,
const unsigned int threadsPerBlock,
int *adj,
bool *visited,
bool *dev_frontier,
int start_port,
int n_ports,
int *dev_flag) {
int *flag = (int *) malloc(sizeof(int));
while (true) {
for (int i = 0; i < n_ports; i++) {
cudaBFSKernel<<<blocks, threadsPerBlock>>>
(adj, dev_frontier, visited, n_ports);
}
cudaContainsTrueKernel<<<blocks, threadsPerBlock>>>
(dev_frontier, dev_flag, n_ports);
cudaMemcpy(flag, dev_flag, sizeof(int), cudaMemcpyDeviceToHost);
if (flag[0]) {
break;
}
}
free(flag);
}
/* Fill out an array, one value for each airport. If an index i is some
* representative node of an SCC (that is not the starting airport) and we have
* that dev_zeroes[i] = 0 at the end of this kernel, then that means that
* index represents an airport who is a representative node of an SCC that has
* no incoming edges.
*/
__global__
void cudaFindDegreeZeroSCCKernel(int *adj,
int *radj,
int *reps,
int *dev_zeroes,
int start_port,
int n_ports) {
unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
dev_zeroes[start_port] = 1;
while (i < n_ports) {
unsigned int curr_rep = reps[i];
for(int j = 0; j < n_ports; j++) {
if (radj[i * n_ports + j] == 1 && reps[j] != curr_rep) {
dev_zeroes[curr_rep] = 1;
break;
}
}
i += blockDim.x * gridDim.x;
}
}
/* Find number of representative nodes that have in-degree 0 (excluding
* starting airport). This is then the final answer to our algorithm.
*/
__global__
void cudaFindAllZeroesKernel(int *dev_reps,
int *dev_zeroes,
int *dev_total,
int n_ports){
unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
while (i < n_ports) {
if(dev_reps[i] == i && dev_zeroes[i] == 0) {
atomicAdd(dev_total, 1);
}
i += blockDim.x * gridDim.x;
}
}
/* Wrapper function to call cudaFindDegreeZeroSCCKernel and
* cudaFindAllZeroesKernel.
*/
void cudaCallFindDegreeZeroSCCKernel(const unsigned int blocks,
const unsigned int threadsPerBlock,
int *dev_adj,
int *dev_radj,
int *dev_reps,
int *dev_zeroes,
int *dev_total,
int start_port,
int n_ports) {
cudaFindDegreeZeroSCCKernel<<<blocks, threadsPerBlock>>>
(dev_adj, dev_radj, dev_reps, dev_zeroes, start_port, n_ports);
cudaFindAllZeroesKernel<<<blocks, threadsPerBlock>>>
(dev_reps, dev_zeroes, dev_total, n_ports);
}
|
18,711 | #include "includes.h"
__global__ void kernel_vecDouble(int *in, int *out, const int n)
{
int i = threadIdx.x;
if (i < n) {
out[i] = in[i] * 2;
}
} |
18,712 | #include <stdio.h>
#include <sys/time.h>
#define ARRAY_SIZE 100000
#define TPB 32
__host__ float cpu_saxpy(int i, float a, float *X, float *Y)
{
return (a*X[i]+Y[i]);
}
__device__ float gpu_saxpy(int i, float a, float *X, float *Y)
{
return (a*X[i]+Y[i]);
}
__global__ void ThreadId(float *y_out, int n, float a, float *X, float *Y)
{
int i = blockIdx.x*blockDim.x + threadIdx.x;
if (i<n)
{
y_out[i]=gpu_saxpy(i,a,X,Y);
}
}
int main()
{
float a=2.0,X[ARRAY_SIZE],*x=0,Y[ARRAY_SIZE],*y=0,Y_out[ARRAY_SIZE],*y_out=0;
int j;
struct timeval time1, time2, time3, time4;
for (j=0;j<ARRAY_SIZE;j++)
{
X[j]=j;
Y[j]=j;
}
gettimeofday(&time1, NULL);
for (j=0;j<ARRAY_SIZE;j++)
{
Y_out[j]=cpu_saxpy(j,a,X,Y);
}
gettimeofday(&time2, NULL);
cudaMalloc(&y_out, ARRAY_SIZE*sizeof(float));
cudaMalloc(&x, ARRAY_SIZE*sizeof(float));
cudaMalloc(&y, ARRAY_SIZE*sizeof(float));
cudaMemcpy(x, X, ARRAY_SIZE*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(y, Y, ARRAY_SIZE*sizeof(float), cudaMemcpyHostToDevice);
int num_block;
num_block= ARRAY_SIZE/TPB;
while (ARRAY_SIZE>num_block*TPB)
{
num_block++;
}
gettimeofday(&time3, NULL);
ThreadId<<<num_block,TPB>>>(y_out, ARRAY_SIZE, a, x, y);
gettimeofday(&time4, NULL);
cudaDeviceSynchronize();
float z[ARRAY_SIZE];
for (j=0;j<ARRAY_SIZE;j++)
{
cudaMemcpy(z, y_out, ARRAY_SIZE*sizeof(float), cudaMemcpyDeviceToHost);
}
cudaFree(y_out);
cudaFree(x);
cudaFree(y);
for (j=0;j<ARRAY_SIZE;j++)
{
printf("CPU:%4.1f GPU:%4.1f Compare:%4.1f\n",Y_out[j],z[j],Y_out[j]-z[j]);
}
printf("CPU Execution: %ldms GPU Execution: %ldms\n",time2.tv_usec-time1.tv_usec,time4.tv_usec-time3.tv_usec);
return 0;
}
|
18,713 | #define NT 1024
// Overall counter variables in global memory.
__device__ int z;
__device__ int m[3];
extern "C" __global__ void modCube (int c, int N){
//Variable declarations
int thr, size, rank;
//Rank and size computations
thr = threadIdx.x;
size = gridDim.x*NT;
rank = blockIdx.x*NT + thr;
for (int x = rank; x < N; x += size){
if (((((x*x)%N)*x)%N) == c){
m[z] = x;
atomicAdd (&z, 1);
}
}
__syncthreads();
} |
18,714 | #include <cuda.h>
#include <iostream>
#include <chrono>
#include <cstring>
#include <cmath>
#define DEBUG 0
//If DEBUG is setted, the program will print the used matrices and the times on the stdout
using namespace std;
/*function marked with '__global__' are the GPU Kernels*/
//This reduces the matrix to upper triangular
__global__ void upperReduction(float *A, float *I, int n, int piv){
int i = blockIdx.x * blockDim.x + threadIdx.x;
int j = blockIdx.y * blockDim.y + threadIdx.y;
float p;
//this is still the same gauss jordan algorithm used in other files
if(i<n && j<n) //to ensure we are within the matrix boundaries
if(i>piv){ // limits operation to rows below the pivot point
p = A[i*n+piv]/A[piv*n+piv];
I[i*n+j] -= I[piv*n+j]*p; // apply for each row member
if(j>=piv){ //limits to row members to the right of the pivot
A[i*n+j] -= A[piv*n+j]*p; // apply only to members right of pivot
}
}
}
//Reduces the matrix to lower triangular matrix
__global__ void lowerReduction(float *A, float *I, int n, int piv){
//same function of before, but row and col are reversed
int i = blockIdx.x * blockDim.x + threadIdx.x;
int j = blockIdx.y * blockDim.y + threadIdx.y;
float p;
if(i<n && j<n)
if(i<piv){
p = A[i*n+piv]/A[piv*n+piv];
I[i*n+j] -= I[piv*n+j]*p;
if(j<=piv){
A[i*n+j] -= A[piv*n+j]*p;
}
}
}
//Scales down the matrix in respect of the elements on the diagonal
__global__ void scale(float *A, float *I, int h){
int row = blockIdx.x * blockDim.x + threadIdx.x;
int col = blockIdx.y * blockDim.y + threadIdx.y;
if(row<h && col<h)//to ensure we are withing not the matrix boundaries
{
I[row*h+col] /= A[row*(h+1)];
A[row*h+col] /= A[row*(h+1)];
}
}
__global__ void matrixMultiplication(float* A, float* B, float* C, int n) {
int row = blockIdx.y*blockDim.y+threadIdx.y;
int col = blockIdx.x*blockDim.x+threadIdx.x;
float c=0;
// each thread computes one element of the block sub-matrix (and therefore one non-overlapping sub-matrix of C)
if (row<n && col<n) { //to ensure we are not withing the matrix boundaries
for (int i = 0; i < n; i++) {
c += A[row*n+i] * B[i*n+col];
}
C[row*n+col] = c;
}
}
/* From utils.cpp */
extern float* createRandomMatrixArray(long, long, bool);
extern float* createIdentityMatrixArray(long);
extern float* createEmptyMatrixArray(long);
extern void print_array_as_matrix(float*, long, char*);
extern void saveTimeToFile(long, float, char*);
extern bool multipliedMatrixCudaIsCorrect(float*, float*, float*, long);
int main(int argc, char **argv){
long min_dim, max_dim, step, dim, data_size; //Used to determine which matrix dimensions we will test
float *A, *B, *C; //After moltiplication , C=A*B
float *D, *M; //M=A, D=Identity and after inversion: D = A^-1, M=Identity
float *gpu_A, *gpu_B, *gpu_C; //GPU Matrices
float *gpu_inv_A, *gpu_inv_I;
float time; //Will contain elapsed time returned by CUDA events, in milliseconds
chrono::high_resolution_clock::time_point start, finish; //Used to implement time measurement
chrono::duration<double> elapsed1, elapsed2; //Used to contain the elapsed time
cudaError_t status; //variable for error handling
cudaEvent_t begin, stop; //used to the time measurement of the functions on the GPU
cudaEventCreate(&begin); //initialize objects
cudaEventCreate(&stop);
// Print the usage command if too few parameters were passed
if(argc != 4){
cout << "Usage: " << argv[0] << " [min_dim] [max_dim] [step]" << endl;
return -1;
}
min_dim = strtol(argv[1], NULL, 10);
max_dim = strtol(argv[2], NULL, 10)+1; //'+1' means we will evaluate the "max_dim" value passed as a argument
step = strtol(argv[3], NULL, 10);
//for each 'dim' from 'min_dim' to 'max_dim', with the step we chosen
for(dim=min_dim;dim<max_dim;dim+=step){
//Matrices are created and used as arrays
A = createRandomMatrixArray(dim, dim, true); //true means "invertible"
B = createRandomMatrixArray(dim, dim, false); //true means "not invertible"
C = createEmptyMatrixArray(dim);
//Number of bytes contained in one matrix
data_size = dim*dim*sizeof(float);
dim3 threadsPerBlock(dim, dim);
dim3 blocksPerGrid(1, 1);
if (dim*dim > 512){ //total amount of threads in a single block cannot exceed 1024 (with a maxwell nVidia GPU)
threadsPerBlock.x = 512;
threadsPerBlock.y = 512;
blocksPerGrid.x = ceil(float(dim)/float(threadsPerBlock.x));
blocksPerGrid.y = ceil(float(dim)/float(threadsPerBlock.y));
}
//allocate memory to contain the matrices
status = cudaMalloc((void**) &gpu_A, data_size);
if(status!=cudaSuccess){
cout << cudaGetErrorString(status) << " in " << __FILE__ << " at line " << __LINE__ << endl;
}
status = cudaMalloc((void**) &gpu_B, data_size);
if(status!=cudaSuccess){
cout << cudaGetErrorString(status) << " in " << __FILE__ << " at line " << __LINE__ << endl;
}
status = cudaMalloc((void**) &gpu_C, data_size);
if(status!=cudaSuccess){
cout << cudaGetErrorString(status) << " in " << __FILE__ << " at line " << __LINE__ << endl;
}
if(DEBUG){
print_array_as_matrix(A,dim,"A ");
print_array_as_matrix(B,dim,"B ");
}
//BEGIN MATRICES MULTIPLICATION
start = chrono::high_resolution_clock::now(); //start time measure
//----------------------CUDA CHARGE CODE----------------------
//copy the matrices A and B from RAM to GPU RAM
status = cudaMemcpy(gpu_A, A, data_size, cudaMemcpyHostToDevice);
status = cudaMemcpy(gpu_B, B, data_size, cudaMemcpyHostToDevice);
cudaDeviceSynchronize(); //to reassure the copy has ended
//----------------------CUDA CHARGE CODE----------------------
finish = chrono::high_resolution_clock::now(); //end time measure
elapsed1 = finish - start; //compute time difference
//elapsed.count() gives the time in seconds
if(DEBUG) cout << "MUL_GCHR: With dimension " << dim << ", elapsed time: " << elapsed1.count() << " s" << endl;
//Save how much time the load took
saveTimeToFile(dim, elapsed1.count(), "csv/load_multiplication_CUDA.csv");
cudaEventRecord(begin, 0); //begin "recording" operations on GPU
//----------------------CUDA PARALLEL CODE----------------------
//load and execute the kernel to multiplication into the GPU
matrixMultiplication <<< blocksPerGrid, threadsPerBlock >>> (gpu_A, gpu_B, gpu_C, dim);
//----------------------CUDA PARALLEL CODE----------------------
cudaDeviceSynchronize(); //GPU kernel calls are asynchronous, so this is necessary
//Find how much time the GPU spent on computing.
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaEventElapsedTime( &time, begin, stop);
if(DEBUG) cout << "MUL_PRLL: With dimension " << dim << ", elapsed time: " << time << " ms" << endl;
start = chrono::high_resolution_clock::now(); //start time measure
//----------------------CUDA DISCHARGE CODE----------------------
//Reading and paste back on RAM the result matrix
status = cudaMemcpy(C, gpu_C, data_size, cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
//----------------------CUDA DISCHARGE CODE----------------------
finish = chrono::high_resolution_clock::now(); //end time measure
elapsed2 = finish - start; //compute time difference
if(DEBUG) cout << "MUL_CCHR: With dimension " << dim << ", elapsed time: " << elapsed2.count() << " s" << endl;
//Save how much time the read of the result took
saveTimeToFile(dim, elapsed2.count(), "csv/read_multiplication_CUDA.csv");
//Save how much time the whole computation took (load+calculations+read)
//Note: 'time' is in milliseconds
saveTimeToFile(dim, elapsed1.count()+elapsed2.count()+time/1000, "csv/multiplication_CUDA.csv");
if(DEBUG){
print_array_as_matrix(C,dim,"C ");
bool correct = multipliedMatrixCudaIsCorrect(A,B,C,dim);
if(!correct){
cout << "Multiplied matrix is not correct, aborting..." << endl;
return -1;
}
}
//Free useless memory on the GPU and on the RAM
cudaFree(gpu_A);
cudaFree(gpu_B);
cudaFree(gpu_C);
free(B);
free(C);
//BEGIN MATRIX INVERSION
D = createIdentityMatrixArray(dim);
//M=A
M = new float[dim*dim];
for (int h = 0; h < dim; h++){
for (int w = 0; w < dim; w++)
M[h*dim+w] = A[h*dim+w];
}
//Number of bytes contained in one matrix
data_size = dim*dim*sizeof(float);
//allocate memory to contain the matrices
status = cudaMalloc((void**) &gpu_inv_A, data_size);
if(status!=cudaSuccess){
cout << cudaGetErrorString(status) << " in " << __FILE__ << " at line " << __LINE__ << endl;
}
status = cudaMalloc((void**) &gpu_inv_I, data_size);
if(status!=cudaSuccess){
cout << cudaGetErrorString(status) << " in " << __FILE__ << " at line " << __LINE__ << endl;
}
start = chrono::high_resolution_clock::now(); //start time measure
//----------------------CUDA CHARGE CODE----------------------
status = cudaMemcpy(gpu_inv_A, M, data_size, cudaMemcpyHostToDevice);
status = cudaMemcpy(gpu_inv_I, D, data_size, cudaMemcpyHostToDevice);
cudaDeviceSynchronize();
//----------------------CUDA CHARGE CODE----------------------
finish = chrono::high_resolution_clock::now(); //end time measure
elapsed1 = finish - start; //compute time difference
if(DEBUG) cout << "INV_GCHR: With dimension " << dim << ", elapsed time: " << elapsed1.count() << " s" << endl;
saveTimeToFile(dim, elapsed1.count(), "csv/load_inversion_CUDA.csv");
cudaEventRecord(begin, 0);
//----------------------CUDA PARALLEL CODE----------------------
//the whole 'for' reduces the matrix to diagonal
//each call computes from a different line of pivot (passed with 'i')
//NOTE: every kernel call waits for the previous one to finish
for(int i=0;i<dim-1;i++){
upperReduction <<< blocksPerGrid, threadsPerBlock >>> (gpu_inv_A, gpu_inv_I, dim, i);
}
for(int i=dim-1;i>0;i--){
lowerReduction <<< blocksPerGrid, threadsPerBlock >>> (gpu_inv_A, gpu_inv_I, dim, i);
}
//this function scales the starting A matrix to the identity, so "I" will be the correct inverse
scale <<< blocksPerGrid, threadsPerBlock >>> (gpu_inv_A, gpu_inv_I, dim); //reduce matrix to diagonal
//----------------------CUDA PARALLEL CODE----------------------
cudaDeviceSynchronize(); //to reassure everything is in sync
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaEventElapsedTime( &time, begin, stop);
if(DEBUG) cout << "INV_PRLL: With dimension " << dim << ", elapsed time: " << time << " ms" << endl;
start = chrono::high_resolution_clock::now(); //start time measure
//----------------------CUDA DISCHARGE CODE----------------------
//Reads back M and D
status = cudaMemcpy(M, gpu_inv_A, data_size, cudaMemcpyDeviceToHost);
status = cudaMemcpy(D, gpu_inv_I, data_size, cudaMemcpyDeviceToHost);
cudaDeviceSynchronize();
//----------------------CUDA DISCHARGE CODE----------------------
finish = chrono::high_resolution_clock::now(); //end time measure
elapsed2 = finish - start; //compute time difference
if(DEBUG) cout << "INV_CCHR: With dimension " << dim << ", elapsed time: " << elapsed2.count() << " s" << endl;
saveTimeToFile(dim, elapsed2.count(), "csv/read_inversion_CUDA.csv");
saveTimeToFile(dim, elapsed1.count()+elapsed2.count()+time/1000, "csv/inversion_CUDA.csv");
if(DEBUG){
print_array_as_matrix(D,dim,"D ");
print_array_as_matrix(M,dim,"M ");
bool correct = multipliedMatrixCudaIsCorrect(A,D,M,dim);
if(!correct){
cout << "Multiplied matrix is not correct, aborting..." << endl;
return -1;
}
}
//deallocate
cudaFree(gpu_inv_A);
cudaFree(gpu_inv_I);
free(A);
free(D);
free(M);
}
cudaEventDestroy(begin);
cudaEventDestroy(stop);
return 0;
}
|
18,715 | #include "includes.h"
__global__ void isnan_check_device(double *array, int size, bool *check) {
//
// Description: Check for nan in array.
int idx = threadIdx.x + blockDim.x * blockIdx.x;
if (idx < size && ::isnan(array[idx])) {
*check = true;
}
} |
18,716 |
/*************************************************
** Accelereyes Training Day 1 **
** Matrix Addition **
** **
** This program will add two matrices and store **
** the result in a third matrix using the GPU **
*************************************************/
#include <iostream>
#include <vector>
#define THREADS 10
using namespace std;
__global__ void add(int *a, int *b, int *c,int columns,int rows)
{
// get the global id for the thread
int x = (blockIdx.x * blockDim.x + threadIdx.x);
int y = (blockIdx.y * blockDim.y + threadIdx.y);
// calculate the index of the input data
int index = y * columns + x;
c[index] = a[index] + b[index];
}
int main(void)
{
int rows = 100;
int columns = 100;
int elements = rows * columns;
size_t size = rows * columns * sizeof(int);
// create device pointers
int* d_a;
int* d_b;
int* d_c;
// allocate memory on the device
cudaMalloc(&d_a, size);
cudaMalloc(&d_b, size);
cudaMalloc(&d_c, size);
// initalize host variables
vector<int> h_a(elements, 5);
vector<int> h_b(elements, 5);
vector<int> h_c(elements);
// transfer the host data to the GPU
cudaMemcpy(d_a, &h_a.front(), size, cudaMemcpyHostToDevice);
cudaMemcpy(d_b, &h_b.front(), size, cudaMemcpyHostToDevice);
// calculate the number of threads and blocks
dim3 threads(THREADS, THREADS);
dim3 blocks(columns/threads.x, rows/threads.y);
// Launch the add kernel
add<<<blocks, threads>>>(d_a, d_b, d_c, columns, rows);
// get the results from the GPU
cudaMemcpy(&h_c.front(), d_c, size, cudaMemcpyDeviceToHost);
// print top left corner
for(int i = 0; i < 5; i++) {
for(int j = 0; j < 10; j++)
cout << h_c[i * rows + j] << " ";
cout << endl;
}
}
|
18,717 | //
// Cuda Sudoku Solver
//
// Created by Arpit Jain
// Copyright (c) 2014 New York University. All rights reserved.
//
#include <stdio.h>
#include <stdlib.h>
#include <curand_kernel.h>
#include <math.h>
#include <cuda.h>
#define NUM_ITERATION 10000
#define INIT_TEMPERATURE 0.4
#define MIN_TEMPERATURE 0.001
#define INIT_TOLERANCE 1
#define DELTA_T 0.2
__constant__ int d_mask[81];
char outname[50];
//Error Checks
#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true)
{
if (code != cudaSuccess)
{
fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort) exit(code);
}
}
// Kernel for initializing random number generators
__global__ void init_random_generator(curandState *state) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
curand_init(1337, idx, 0, &state[idx]);
}
// This functions returns the count of number of unique elements in a row or column number according to the flag (Device Version)
__device__ int d_num_unique(int rc_num,int sudoku[][9],int flag)
{
int nums[9]={1,2,3,4,5,6,7,8,9};
int idx, unique_Count;
unique_Count = 0;
for(int j=0;j<9;j++)
{
if(flag==2)
idx = sudoku[j][rc_num]-1;
else
idx = sudoku[rc_num][j]-1;
if(idx==-1)
return -1;
if(nums[idx]!=0)
{
unique_Count+=1;
nums[idx]=0;
}
}
return unique_Count;
}
//Computes the energy by adding the number of unique elements in all the rows and columns
__device__ int d_compute_energy(int sudoku[][9])
{
int energy=0;
for(int i=0;i<9;i++)
energy += d_num_unique(i,sudoku,1) + d_num_unique(i,sudoku,2);
return 162-energy;
}
//Kernel to run a Markov chain
__global__ void markov(int* sudoku,curandState *state,int cur_energy,float temperature,int *b1,int *b2,int *b3,int *b4,int *b5,int *b6,int *b7,int *b8,int *b9,int *b10,int *b11,int *b12,int *b13,int *b14,int *b15,int *energy_block)
{
__shared__ int shd_sudoku[9][9];
int thread_x=threadIdx.x;
int thread_y=threadIdx.y;
int thread_num_local= threadIdx.x*blockDim.x + threadIdx.y;
int block_num= blockIdx.x*blockDim.x + blockIdx.y;
//Bring the sudoku to shared memory
shd_sudoku[thread_x][thread_y]=sudoku[thread_x+ 9*thread_y];
if(thread_num_local!=0)
{
return;
}
int block_x;
int block_y;
int r1_x, r1_y, r2_x, r2_y;
int temp;
int energy;
for(int iter=0;iter<NUM_ITERATION;iter++)
{
//Select a Random sub block in the sudoku
block_x = 3*(int)(3.0*curand_uniform(&state[block_num]));
block_y = 3*(int)(3.0*curand_uniform(&state[block_num]));
//Select two unmasked points
do
{
r1_x=(int)3.0*curand_uniform(&state[block_num]);
r1_y=(int)3.0*curand_uniform(&state[block_num]);
}while(d_mask[(block_x+r1_x)+9*(block_y+r1_y)]==1);
do{
r2_x=(int)3.0*curand_uniform(&state[block_num]);
r2_y=(int)3.0*curand_uniform(&state[block_num]);
}while(d_mask[(block_x+r2_x)+9*(block_y+r2_y)]==1);
//Swap the elements
temp=shd_sudoku[block_x+r1_x][block_y+r1_y];
shd_sudoku[block_x+r1_x][block_y+r1_y]=shd_sudoku[block_x+r2_x][block_y+r2_y];
shd_sudoku[block_x+r2_x][block_y+r2_y]=temp;
//Compute the energy of this new state
energy=d_compute_energy(shd_sudoku);
if(energy<cur_energy)
cur_energy = energy;
else{
//Accept the state
if(exp((float)(cur_energy-energy)/temperature)>curand_uniform(&state[block_num]))
cur_energy = energy;
// if(cur_energy-energy>0.2)
// cur_energy = energy;
//Reject the state and undo changes
else{
temp=shd_sudoku[block_x+r1_x][block_y+r1_y];
shd_sudoku[block_x+r1_x][block_y+r1_y]=shd_sudoku[block_x+r2_x][block_y+r2_y];
shd_sudoku[block_x+r2_x][block_y+r2_y]=temp;
}
}
//If reached the lowest point break
if(energy==0)
break;
}
//Write the result back to memory
for(int i=0;i<9;i++)
{
for(int j=0;j<9;j++)
{
if(block_num==0)
b1[i+9*j]=shd_sudoku[i][j];
if(block_num==1)
b2[i+9*j]=shd_sudoku[i][j];
if(block_num==2)
b3[i+9*j]=shd_sudoku[i][j];
if(block_num==3)
b4[i+9*j]=shd_sudoku[i][j];
if(block_num==4)
b5[i+9*j]=shd_sudoku[i][j];
if(block_num==5)
b6[i+9*j]=shd_sudoku[i][j];
if(block_num==6)
b7[i+9*j]=shd_sudoku[i][j];
if(block_num==7)
b8[i+9*j]=shd_sudoku[i][j];
if(block_num==8)
b9[i+9*j]=shd_sudoku[i][j];
if(block_num==9)
b10[i+9*j]=shd_sudoku[i][j];
if(block_num==10)
b11[i+9*j]=shd_sudoku[i][j];
if(block_num==11)
b12[i+9*j]=shd_sudoku[i][j];
if(block_num==12)
b13[i+9*j]=shd_sudoku[i][j];
if(block_num==13)
b14[i+9*j]=shd_sudoku[i][j];
if(block_num==14)
b15[i+9*j]=shd_sudoku[i][j];
}
}
//Write the energy back to memory for the current state
energy_block[block_num]=cur_energy;
}
//Display the sudoku
void display_sudoku(int *n){
printf("\n_________________________\n");
for(int i=0;i<9;i++){
printf("| ");
for(int j=0;j<9;j=j+3)
printf("%1d %1d %1d | ",n[i+9*j],n[i+9*(j+1)],n[i+9*(j+2)]);
if((i+1)%3==0){
printf("\n-------------------------\n");
}else printf("\n");
}
return;
}
/*Initialize the sudoku. 1) Read the partial sudoku.
2) Place values in all the empty slots such that the 3x3 subgrid clause is satisfied */
void init_sudoku(int *s,int *m,char* fname)
{
FILE *fin ;
fin = fopen(fname,"r");
//Output file name
int len;
for(len=0;len<strlen(fname)-2;len++)
outname[len]=fname[len];
strcat(outname,"out");
int in;
int x, y;
int p, q;
int idx;
int nums_1[9],nums_2[9];
//Read the partial sudoku from file
//Compute the mask. 0 -> mutable value 1-> non-mutable
for(int i=0;i<9;i++){
for(int j=0;j<9;j++){
fscanf(fin,"%1d",&in);
s[i+9*j] = in;
if(in==0)
m[i+9*j]=0;
else
m[i+9*j]=1;
}
}
fclose(fin);
printf("Puzzle\n");
display_sudoku(s);
//Place values in all the empty slots such that the 3x3 subgrid clause is satisfied
for(int block_i=0;block_i<3;block_i++)
{
for(int block_j=0;block_j<3;block_j++)
{
for(int k=0;k<9;k++)
nums_1[k]=k+1;
for(int i=0;i<3;i++)
{
for(int j=0;j<3;j++)
{
x = block_i*3 + i;
y = block_j*3 + j;
if(s[x+9*y]!=0){
p = s[x+9*y];
nums_1[p-1]=0;
}
}
}
q = -1;
for(int k=0;k<9;k++)
{
if(nums_1[k]!=0)
{
q+=1;
nums_2[q] = nums_1[k];
}
}
idx = 0;
for(int i=0;i<3;i++)
{
for(int j=0;j<3;j++)
{
x = block_i*3 + i;
y = block_j*3 + j;
if(s[x+9*y]==0)
{
s[x+9*y] = nums_2[idx];
idx+=1;
}
}
}
}
}
}
// This functions returns the count of number of unique elements in a row or column number according to the flag (Host Version)
int h_num_unique(int i, int k, int *n){
int nums[9]={1,2,3,4,5,6,7,8,9};
int idx, unique_count;
unique_count = 0;
for(int j=0;j<9;j++){
if(k==1){
idx = n[i+9*j]-1;
}
else{
idx = n[j+9*i]-1;
}
if(idx==-1){
return -1;
}
if(nums[idx]!=0){
unique_count+=1;
nums[idx]=0;
}
}
return unique_count;
}
//Computes the energy by adding the number of unique elements in all the rows and columns
int h_compute_energy(int *n)
{
int energy = 0;
for(int i=0;i<9;i++){
energy += h_num_unique(i,1,n) + h_num_unique(i,2,n);
}
return 162 - energy;
}
void write_file(int *s)
{
FILE *fout;
fout=fopen(outname,"w");
for(int i=0;i<9;i++)
{
for(int j=0;j<9;j++)
fprintf(fout,"%1d",s[i+9*j]);
if(i<8)
fprintf(fout,"\n");
}
fclose(fout);
}
//Main
int main(int arg,char* argv[]) {
//cudaSetDevice(0);
//cudaDeviceSetCacheConfig(cudaFuncCachePreferL1);
int device;
cudaGetDevice(&device);
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop,device);
//Tunable Parameter
int num_chains;
if(prop.multiProcessorCount>=15)
num_chains=15;
else
num_chains=prop.multiProcessorCount;
float temperature=INIT_TEMPERATURE;
float temp_min=MIN_TEMPERATURE;
//Host pointers
int *sudoku;
int *mask;
int *h_energy_host;
int size=sizeof(int)*81;
//Allocate memory
gpuErrchk(cudaHostAlloc((void**)&sudoku,size,cudaHostAllocDefault));
gpuErrchk(cudaHostAlloc((void**)&mask,size,cudaHostAllocDefault));
gpuErrchk(cudaHostAlloc((void**)&h_energy_host,sizeof(int)*num_chains,cudaHostAllocDefault));
init_sudoku(sudoku,mask,argv[1]);
//Initial Energy of sudoku
int current_energy=h_compute_energy(sudoku);
printf("Current energy %d \n",current_energy);
//Device pointers
int *d_sudoku;
int *d_b1,*d_b2,*d_b3,*d_b4,*d_b5,*d_b6,*d_b7,*d_b8,*d_b9,*d_b10,*d_b11,*d_b12,*d_b13,*d_b14,*d_b15;
int *energy_block;
//Allocate memory
gpuErrchk(cudaMalloc((void**)&d_sudoku,size));
gpuErrchk(cudaMalloc((void**)&d_mask,size));
gpuErrchk(cudaMalloc((void**)&d_b1,size));
gpuErrchk(cudaMalloc((void**)&d_b2,size));
gpuErrchk(cudaMalloc((void**)&d_b3,size));
gpuErrchk(cudaMalloc((void**)&d_b4,size));
gpuErrchk(cudaMalloc((void**)&d_b5,size));
gpuErrchk(cudaMalloc((void**)&d_b6,size));
gpuErrchk(cudaMalloc((void**)&d_b7,size));
gpuErrchk(cudaMalloc((void**)&d_b8,size));
gpuErrchk(cudaMalloc((void**)&d_b9,size));
gpuErrchk(cudaMalloc((void**)&d_b10,size));
gpuErrchk(cudaMalloc((void**)&d_b11,size));
gpuErrchk(cudaMalloc((void**)&d_b12,size));
gpuErrchk(cudaMalloc((void**)&d_b13,size));
gpuErrchk(cudaMalloc((void**)&d_b14,size));
gpuErrchk(cudaMalloc((void**)&d_b15,size));
gpuErrchk(cudaMalloc((void**)&energy_block,sizeof(int)*num_chains));
//Copy Sudoku and Mask to GPU
gpuErrchk(cudaMemcpy(d_sudoku,sudoku,size,cudaMemcpyHostToDevice));
gpuErrchk(cudaMemcpyToSymbol(d_mask,mask,size));
//Grid and Block dimensions
dim3 dimGrid(1,num_chains);
dim3 dimBlock(9,9);
printf("Solution");
//Random number generators. Launch init_random_generator kernel
curandState *d_state;
gpuErrchk(cudaMalloc(&d_state, dimBlock.x* dimBlock.y * dimGrid.x * dimGrid.y));
init_random_generator<<<dimGrid.x * dimGrid.y, dimBlock.x* dimBlock.y>>>(d_state);
gpuErrchk(cudaPeekAtLastError());
gpuErrchk(cudaDeviceSynchronize());
int tolerance=INIT_TOLERANCE;
int min,min_idx;
int e;
int prev_energy=current_energy;
//Simulated Annealing loop
do{
min=200;
min_idx=200;
markov<<< dimGrid,dimBlock >>>(d_sudoku,d_state,current_energy,temperature,d_b1,d_b2,d_b3,d_b4,d_b5,d_b6,d_b7,d_b8,d_b9,d_b10,d_b11,d_b12,d_b13,d_b14,d_b15,energy_block);
gpuErrchk(cudaDeviceSynchronize());
cudaMemcpy(h_energy_host,energy_block,sizeof(int)*num_chains,cudaMemcpyDeviceToHost);
for(e=0;e<num_chains;e++)
{
if(h_energy_host[e]<min)
{
min=h_energy_host[e];
min_idx=e;
}
}
if(min_idx==0)
{
cudaMemcpy(d_sudoku,d_b1,size,cudaMemcpyDeviceToDevice);
current_energy=min;
}
if(min_idx==1)
{
cudaMemcpy(d_sudoku,d_b2,size,cudaMemcpyDeviceToDevice);
current_energy=min;
}
if(min_idx==2)
{
cudaMemcpy(d_sudoku,d_b3,size,cudaMemcpyDeviceToDevice);
current_energy=min;
}
if(min_idx==3)
{
cudaMemcpy(d_sudoku,d_b4,size,cudaMemcpyDeviceToDevice);
current_energy=min;
}
if(min_idx==4)
{
cudaMemcpy(d_sudoku,d_b5,size,cudaMemcpyDeviceToDevice);
current_energy=min;
}
if(min_idx==5)
{
cudaMemcpy(d_sudoku,d_b6,size,cudaMemcpyDeviceToDevice);
current_energy=min;
}
if(min_idx==6)
{
cudaMemcpy(d_sudoku,d_b7,size,cudaMemcpyDeviceToDevice);
current_energy=min;
}
if(min_idx==7)
{
cudaMemcpy(d_sudoku,d_b8,size,cudaMemcpyDeviceToDevice);
current_energy=min;
}
if(min_idx==8)
{
cudaMemcpy(d_sudoku,d_b9,size,cudaMemcpyDeviceToDevice);
current_energy=min;
}
if(min_idx==9)
{
cudaMemcpy(d_sudoku,d_b10,size,cudaMemcpyDeviceToDevice);
current_energy=min;
}
if(min_idx==10)
{
cudaMemcpy(d_sudoku,d_b11,size,cudaMemcpyDeviceToDevice);
current_energy=min;
}
if(min_idx==11)
{
cudaMemcpy(d_sudoku,d_b12,size,cudaMemcpyDeviceToDevice);
current_energy=min;
}
if(min_idx==12)
{
cudaMemcpy(d_sudoku,d_b13,size,cudaMemcpyDeviceToDevice);
current_energy=min;
}
if(min_idx==13)
{
cudaMemcpy(d_sudoku,d_b14,size,cudaMemcpyDeviceToDevice);
current_energy=min;
}
if(min_idx==14)
{
cudaMemcpy(d_sudoku,d_b15,size,cudaMemcpyDeviceToDevice);
current_energy=min;
}
if(current_energy==0)
{
break;
}
if(current_energy==prev_energy)
tolerance--;
else
tolerance=INIT_TOLERANCE;
// Random restart if energy is stuck
if(tolerance<0)
{
//printf("Randomizing\n");
cudaMemcpy(sudoku,d_sudoku,size,cudaMemcpyDeviceToHost);
int ar[3]={0,3,6};
int tempa;
int rand1=random()%3;
int rand2=random()%3;
int r1_x,r1_y,r2_x,r2_y;
int block_x,block_y;
for(int suf=0;suf<random()%10;suf++)
{
block_x = ar[rand1];
block_y = ar[rand2];
do{
r1_x=random()%3;
r1_y=random()%3;;
}while(mask[(block_x+r1_x)+9*(block_y+r1_y)]==1);
do{
r2_x=random()%3;;
r2_y=random()%3;;
}while(mask[(block_x+r2_x)+9*(block_y+r2_y)]==1);
tempa=sudoku[(block_x+r1_x)+9*(block_y+r1_y)];
sudoku[(block_x+r1_x)+9*(block_y+r1_y)]=sudoku[(block_x+r2_x)+9*(block_y+r2_y)];
sudoku[(block_x+r2_x)+9*(block_y+r2_y)]=tempa;
}
cudaMemcpy(d_sudoku,sudoku,size,cudaMemcpyHostToDevice);
current_energy=h_compute_energy(sudoku);
//printf("Energy after randomizing %d \n",current_energy);
tolerance=INIT_TOLERANCE;
temperature=temperature+DELTA_T;
}
prev_energy=current_energy;
if(current_energy==0)
{
break;
}
temperature=temperature*0.8;
//printf("Energy after temp %f is %d \n",temperature,current_energy);
}while(temperature>temp_min);
cudaMemcpy(sudoku,d_sudoku,size,cudaMemcpyDeviceToHost);
display_sudoku(sudoku);
write_file(sudoku);
current_energy=h_compute_energy(sudoku);
printf("Current energy %d \n",current_energy);
return 0;
}
|
18,718 | #include "includes.h"
__global__ void TgvComputeOpticalFlowVectorMaskedKernel(const float *u, const float2 *tv2, float* mask, int width, int height, int stride, float2 *warpUV)
{
const int ix = threadIdx.x + blockIdx.x * blockDim.x;
const int iy = threadIdx.y + blockIdx.y * blockDim.y;
if ((iy >= height) && (ix >= width)) return;
int pos = ix + iy * stride;
if (mask[pos] == 0.0f) return;
float us = u[pos];
float2 tv2s = tv2[pos];
warpUV[pos].x = us * tv2s.x;
warpUV[pos].y = us * tv2s.y;
} |
18,719 | #include "includes.h"
__global__ void callOperationSharedStatic(int *a, int *b, int *c, int n)
{
int tid = blockDim.x * blockIdx.x + threadIdx.x;
if (tid >= n) {
return;
}
__shared__ int s_a[size], s_b[size], s_c[size];
s_a[tid] = a[tid];
s_b[tid] = b[tid];
if (s_a[tid] <= s_b[tid])
{
s_c[tid] = s_a[tid];
}
else
{
s_c[tid] = s_b[tid];
}
c[tid] = s_c[tid];
} |
18,720 | #include <cstdlib>
#include<iostream>
#include<cuda.h>
#include <sys/time.h>
#include <cuda_fp16.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <unistd.h>
#include <cuda_fp16.h>
#define cudaCores 3584
using namespace std;
FILE *fp;
int smCount,totalThreads;
//__float2half
/*void getGPUConfig(){
cudaDeviceProp cudaProg;
cudaGetDeviceProperties(&cudaProg,0);
int SMCount=cudaProg.multiProcessorCount;
int threadPerBlock=cudaProg.maxThreadsPerBlock;
int maxThreads=SMCount*threadPerBlock;
int numberOfBlolcks=__gcd(maxThreads,threadPerBlock);
int numberOfThreads=maxThreads/numberOfBlolcks;
cout << "number of blocks:"<< numberOfBlolcks<< endl;
cout << "number of threads:"<< numberOfThreads<< endl;
}*/
__global__ void multiplyInt(int *a,int *b,int n){
int i=blockIdx.x*blockDim.x+threadIdx.x;
if(i<n){
b[i]=b[i]+a[i];
}
}
__global__ void multiplyFloat(float *a,float *b,int n){
int i=blockIdx.x*blockDim.x+threadIdx.x;
if(i<n){
b[i]=b[i]+a[i];
}
}
__global__ void multiplyHalfFloat(half *a,half *b,int n){
int i=blockIdx.x*blockDim.x+threadIdx.x;
if(i<n){
b[i]=__float2half(__half2float(a[i])+__half2float(a[i]));
}
}
int main()
{
fp= fopen( "GPULogs.txt", "ab" );
cudaDeviceProp prop;
cudaGetDeviceProperties(&prop, 0);
cout<< "Device Name:"<<prop.name<<endl;
fprintf(fp,"\nDeviceName:%s",prop.name);
cout << "Max Threads Per Block:"<<prop.maxThreadsPerBlock<<endl;
fprintf(fp,"\nMax Threads Per Block:%d",prop.maxThreadsPerBlock);
smCount = prop.multiProcessorCount;
cout << "SM Count is:"<<smCount<<endl;
fprintf(fp,"\nSM Count:%d",smCount);
cout <<"Warp Size:"<< prop.warpSize<<endl;
fprintf(fp,"\nWarp Size:%d",prop.warpSize);
cout << "Clock Rate:"<< prop.clockRate<<endl;
fprintf(fp,"\nClock Rate:%d",prop.clockRate);
totalThreads = smCount * cudaCores;
cout << "Total Number of Threads:"<< totalThreads<<endl;
int SIZE=totalThreads;
int *a,*b;
int *d_a,*d_b;
float *a_f,*b_f;
float *d_a_f,*d_b_f;
float *a_half,*b_half;
half *d_a_half,*d_b_half;
struct timeval start_int, end_int;
struct timeval start_float, end_float;
struct timeval start_half_float, end_half_float;
a= new int[SIZE];
b=new int[SIZE];
a_f= new float[SIZE];
b_f=new float[SIZE];
a_half=new float[SIZE];
b_half=new float[SIZE];
cudaMalloc(&d_a, SIZE*sizeof(int));
cudaMalloc(&d_b, SIZE*sizeof(int));
cudaMalloc(&d_a_f, SIZE*sizeof(float));
cudaMalloc(&d_b_f, SIZE*sizeof(float));
cudaMalloc(&d_a_half, SIZE*sizeof(half));
cudaMalloc(&d_b_half, SIZE*sizeof(half));
int i;
for (i = 0; i < SIZE;i++) {
a[i] = i;
b[i] = 1;
}
for (i= 0;i< SIZE;i++) {
a_f[i] = i+0.5;
b_f[i] = i+1.5;
}
for (i= 0;i< SIZE;i++) {
a_half[i] = i+1.05;
b_half[i] = i+2.05;
}
cudaMemcpy(d_a, a,SIZE*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_b, b,SIZE*sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_a_f,a_f,SIZE*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_b_f,b_f,SIZE*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(d_a_half,a_half,SIZE*sizeof(half), cudaMemcpyHostToDevice);
cudaMemcpy(d_b_half,b_half,SIZE*sizeof(half), cudaMemcpyHostToDevice);
gettimeofday(&start_int, NULL);
for(i=0;i<1000;i++){
multiplyInt<<<smCount,cudaCores >>>(d_a,d_b,SIZE);
}
gettimeofday(&end_int, NULL);
cudaMemcpy(b, d_b, SIZE*sizeof(int), cudaMemcpyDeviceToHost);
gettimeofday(&start_float, NULL);
for(i=0;i<1000;i++){
multiplyFloat<<<smCount,cudaCores>>>(d_a_f,d_b_f,SIZE);
}
gettimeofday(&end_float, NULL);
cudaMemcpy(b_f, d_b_f, SIZE*sizeof(float),cudaMemcpyDeviceToHost);
gettimeofday(&start_half_float, NULL);
for(i=0;i<1000;i++){
multiplyHalfFloat<<<smCount,cudaCores>>>(d_a_half,d_b_half,SIZE);
}
gettimeofday(&end_half_float, NULL);
cudaMemcpy(b_half, d_b_half, SIZE*sizeof(float),cudaMemcpyDeviceToHost);
float IOPS = ((SIZE*1000*cudaCores)/ ((1000.0 * (end_int.tv_sec - start_int.tv_sec) + (end_int.tv_usec - start_int.tv_usec) / 1000.0)/1000)/1e9);
cout << "IOPS:"<< IOPS<< endl;
float FLOPS = ((SIZE*1000*cudaCores)/ ((1000.0 * (end_float.tv_sec - start_float.tv_sec) + (end_float.tv_usec - start_float.tv_usec) / 1000.0)/1000)/1e9);
cout << "GFLOPS:"<< FLOPS << endl;
float GHOPS = ((SIZE*1000*cudaCores)/ ((1000.0 * (end_half_float.tv_sec - start_half_float.tv_sec) + (end_half_float.tv_usec - start_half_float.tv_usec) / 1000.0)/1000)/1e9);
cout << "GHOPS:"<< GHOPS << endl;
fprintf(fp,"\nGFLOPS for %s:%f",prop.name,FLOPS);
fprintf(fp,"\nIOPS for %s:%f",prop.name,IOPS);
fprintf(fp,"\nGHOPS for %s:%f",prop.name,GHOPS);
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_a_f);
cudaFree(d_b_f);
cudaFree(d_b_half);
cudaFree(d_a_half);
return 0;
}
|
18,721 | #include <stdlib.h>
#include <stdio.h>
// CUDA kernel
__global__ void say_hello()
{
// a CUDA core executes this line
printf("GPU says, Hello world!\n");
}
// CUDA kernel for step 2
__global__ void say_hello2()
{
// a CUDA core executes this line
printf("Thread %d says, Hello world!\n", threadIdx.x);
}
// CUDA kernel for step 3
__global__ void say_hello3()
{
// a CUDA core executes this line
printf("Thread %d in block %d says, Hello world!\n",
threadIdx.x, blockIdx.x);
}
// CUDA kernel for step 4
__global__ void say_hello4()
{
// a CUDA core executes this line
if (blockIdx.x == 0)
printf("Thread %d in block %d says, Hello world!\n",
threadIdx.x, blockIdx.x);
else
printf("Thread %d in block %d says, Hello Umea!\n",
threadIdx.x, blockIdx.x);
}
// CUDA kernel for step 5
__global__ void say_hello5()
{
// a CUDA core executes this line
int thread_id = blockIdx.x * blockDim.x + threadIdx.x;
printf("Thread %d says, Hello world!\n", thread_id);
}
// CUDA kernel for step 6
__global__ void say_hello6()
{
// a CUDA core executes this line
printf("Thread (%d,%d) in block (%d,%d) says, Hello world!\n",
threadIdx.x, threadIdx.y, blockIdx.x, blockIdx.y);
}
int main()
{
// a CPU core executes these lines
printf("====== Step 1 ======\n");
printf("Host says, Hello world!\n");
// call the kernel
say_hello<<<1,1>>>();
// wait until the GPU has executed the kernel
cudaDeviceSynchronize();
printf("====== Step 2 ======\n");
printf("Host says, Hello world!\n");
say_hello2<<<1,16>>>();
cudaDeviceSynchronize();
printf("====== Step 3 ======\n");
printf("Host says, Hello world!\n");
say_hello3<<<2,16>>>();
cudaDeviceSynchronize();
printf("====== Step 4 ======\n");
printf("Host says, Hello world!\n");
say_hello4<<<2,16>>>();
cudaDeviceSynchronize();
printf("====== Step 5 ======\n");
printf("Host says, Hello world!\n");
say_hello5<<<2,16>>>();
cudaDeviceSynchronize();
printf("====== Step 6 ======\n");
printf("Host says, Hello world!\n");
dim3 threads(4, 2);
dim3 blocks(2, 3);
say_hello6<<<blocks,threads>>>();
cudaDeviceSynchronize();
printf("====== Step 7 ======\n");
printf("Host says, Hello world!\n");
//dim3 threads(4, 2);
//dim3 blocks(2, 3);
say_hello6<<<blocks,threads>>>();
return EXIT_SUCCESS;
}
|
18,722 | #include <stdio.h>
#define imin(a,b)(a<b?a:b)
const int N = 33 * 1024;
const int threadsPerBlock = 256;
const int blocksPerGrid = imin(32, (N + threadsPerBlock - 1) / threadsPerBlock);
__global__ void dot(float *a, float *b, float *c)
{
__shared__ float cache[threadsPerBlock];
int tid = threadIdx.x + blockIdx.x * blockDim.x;
int cache_index = threadIdx.x;
float temp = 0;
while(tid < N)
{
temp += a[tid] * b[tid];
tid += blockDim.x * gridDim.x;
}
// set the cache values
cache[cache_index] = temp;
// synchronize threads in this block
__syncthreads();
// ----- reduce for sum ---------------------
int i = blockDim.x/2;
while(i != 0)
{
if(cache_index < i)
{
cache[cache_index] += cache[cache_index + i];
__syncthreads();
}
i /= 2;
}
if (cache_index == 0)
{
c[blockIdx.x] = cache[0];
}
}
int main(void)
{
float *a, *b, c, *partial_c;
float *dev_a, *dev_b, *dev_partial_c;
//Allocate memory on the CPU
a = (float*) malloc(N*sizeof(float));
b = (float*) malloc(N*sizeof(float));
partial_c = (float*) malloc(blocksPerGrid*sizeof(float));
cudaMalloc((void**)&dev_a, N*sizeof(float));
cudaMalloc((void**)&dev_b, N*sizeof(float));
cudaMalloc((void**)&dev_partial_c,blocksPerGrid*sizeof(float));
//fill data
for (int i=0; i<N; i++)
{
a[i] = i;
b[i] = i*2;
}
//Copy the arrays on the GPU
cudaMemcpy(dev_a, a, N*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dev_b, b, N*sizeof(float), cudaMemcpyHostToDevice);
dot<<<blocksPerGrid,threadsPerBlock>>>(dev_a, dev_b, dev_partial_c);
cudaMemcpy(partial_c, dev_partial_c, blocksPerGrid*sizeof(float), cudaMemcpyDeviceToHost);
c = 0;
for(int i=0; i<blocksPerGrid; i++)
{
c += partial_c[i];
}
#define sum_squares(x)(x*(x+1)*(2*x+1)/6)
printf("Does GPU value %.6g = %.6g?\n", c, 2 * sum_squares( (float)(N - 1) ) );
// free memory on the GPU side
cudaFree( dev_a );
cudaFree( dev_b );
cudaFree( dev_partial_c );
// free memory on the CPU side
free( a );
free( b );
free( partial_c );
}
|
18,723 |
/*
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
//#include "helper_cuda.h"
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#define SIZE_M (512 * 2)
#define SIZE_N (512 * 4)
#define SIZE_K (512 * 2)
#define BLOCK_SIZE 16
#define ID2INDX(_row, _col, _width) (((_row)*(_width))+(_col))
void generateValues(float** p, int size) {
for (int i = 0; i < size; i++) {
*((*p) + i) = rand() % 10 + rand() % 100 / 100.0;
}
}
__global__ void MatMul(float* matA, float* matB, float* matC, int m, int n, int k) {
int row = blockDim.x * blockIdx.x + threadIdx.x;
int col = blockDim.y * blockIdx.y + threadIdx.y;
if (row >= m || col >= n) return;
float val = 0;
for (int i = 0; i < k; i++) {
val += matA[ID2INDX(row, i, k)] * matB[ID2INDX(i, col, n)];
}
matC[ID2INDX(row, col, n)] = val;
}
void compareMatrix(float* a, float* b, int size) {
bool isPass = true;
for (int i = 0; i < size; i++) {
if (a[i] != b[i]) {
isPass = false;
//break;
//printf("%d != %d\n", a[i], b[i]);
}
}
if (isPass) printf("CPU and GPU result are same.\n");
else printf("The results are not matched!!!!\n");
}
int main(int argc, char* argv[]) {
// Matrix size
int m, n, k;
m = SIZE_M; n = SIZE_N; k = SIZE_K;
printf("Matrix A = [%d by %d]\nMatrix B = [%d by %d]\nMatrix C = [%d by %d]\n",
m, k, k, n, m, n);
int sizeA, sizeB, sizeC;
sizeA = m * k; sizeB = n * k; sizeC = m * n;
// initialize matrix A and B
float* A = NULL, * B = NULL;
A = new float[sizeA];
B = new float[sizeB];
memset(A, 0, sizeof(float) * sizeA);
memset(B, 0, sizeof(float) * sizeB);
// initialize matrix cpuC and gpuC
float* cpuC = NULL, * gpuC = NULL;
cpuC = new float[sizeC];
gpuC = new float[sizeC];
memset(cpuC, 0, sizeof(float) * sizeC);
memset(gpuC, 0, sizeof(float) * sizeC);
// input values matrix A and B
generateValues(&A, sizeA);
generateValues(&B, sizeB);
printf("CPU val: %f %f\n", A[0], B[0]);
// CPU running
for (int row = 0; row < m; row++) {
for (int col = 0; col < n; col++) {
int index = ID2INDX(row, col, n);
for (int i = 0; i < k; i++) {
cpuC[index] += A[ID2INDX(row, i, k)] * B[ID2INDX(i, col, n)];
}
}
}
printf("CPU result %f\n", cpuC[0]);
printf("CPU is finished\n");
// GPU
printf("GPU start\n");
float* dA, * dB, * dC;
cudaMalloc(&dA, sizeA * sizeof(float));
cudaMalloc(&dB, sizeB * sizeof(float));
cudaMalloc(&dC, sizeC * sizeof(float));
cudaMemset(&dA, 0, sizeA * sizeof(float));
cudaMemset(&dB, 0, sizeB * sizeof(float));
cudaMemset(&dC, 0, sizeC * sizeof(float));
cudaMemcpy(dA, A, sizeA * sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(dB, B, sizeB * sizeof(float), cudaMemcpyHostToDevice);
printf("finished copy data from host to device\n");
dim3 gridDim(ceil((float)m / BLOCK_SIZE), ceil((float)n / BLOCK_SIZE));
dim3 blockDim(BLOCK_SIZE, BLOCK_SIZE);
printf("Grid(%d, %d), Block(%d, %d)\n", gridDim.x, gridDim.y, blockDim.x, blockDim.y);
MatMul << <gridDim, blockDim >> > (dA, dB, dC, m, n, k);
cudaDeviceSynchronize();
cudaMemcpy(gpuC, dC, sizeC * sizeof(float), cudaMemcpyDeviceToHost);
printf("GPU result %f\n", gpuC[0]);
compareMatrix(cpuC, gpuC, sizeC);
// 1. GPU global memory
return 0;
}
*/ |
18,724 | #include <iostream>
#include <math.h>
#define cudaCheckError() { \
cudaError_t e=cudaGetLastError(); \
if(e!=cudaSuccess) { \
printf("Cuda failure %s:%d: '%s'\n",__FILE__,__LINE__,cudaGetErrorString(e)); \
exit(EXIT_FAILURE); \
} \
}
// CUDA kernel to add elements of two arrays
__global__ void add(float *x, float *y)
{
//@@ add a single element of x and y and store the result in y
int i = blockDim.x * blockIdx.x + threadIdx.x; // FIXME derive from block and thread info the index of the value to set
y[i] += x[i];
}
int main(void)
{
int N = 1<<20; // 1M elements
float *x, *y;
// Allocate Unified Memory -- accessible from CPU or GPU
cudaMallocManaged(&x, N*sizeof(float));
cudaMallocManaged(&y, N*sizeof(float));
// initialize x and y arrays on the host
for (int i = 0; i < N; i++) {
x[i] = 1.0f;
y[i] = 2.0f;
}
// Launch kernel on 1M elements on the GPU
int blockSize = 256;
int numBlocks = (N + blockSize - 1) / blockSize;
add<<<numBlocks, blockSize>>>(x, y);
// Wait for GPU to finish before accessing on host
cudaDeviceSynchronize();
cudaCheckError();
// Check for errors (all values should be 3.0f)
float maxError = 0.0f;
for (int i = 0; i < N; i++)
maxError = fmax(maxError, fabs(y[i]-3.0f));
std::cout << "Max error: " << maxError << std::endl;
// Free memory
cudaFree(x);
cudaFree(y);
cudaCheckError();
if (maxError < 0.000001f) {
printf("Test completed successfully.\n");
return 0;
} else {
printf("WARNING there were some errors.\n");
return 1;
}
} |
18,725 | #include "includes.h"
#define THREADS_PER_BLOCK 256
__global__ void MatrixMul( float *Md , float *Nd , float *Pd , const int WIDTH )
{
int COL = threadIdx.x + blockIdx.x * blockDim.x;
int ROW = threadIdx.y + blockIdx.y * blockDim.y;
if (ROW < WIDTH && COL < WIDTH) {
for (int i = 0; i < WIDTH; i++) {
Pd[ROW * WIDTH + COL] += Md[ROW * WIDTH + i] * Nd [i * WIDTH + COL];
}
}
} |
18,726 | //#include <stdio.h>
//#include <string.h>
//int main()
//{
//
//
//
//} |
18,727 | extern "C" __global__ void transpose(int* A, int* B, int rows, int cols)
{
int col = threadIdx.x + blockIdx.x * blockDim.x;
int row = threadIdx.y + blockIdx.y * blockDim.y;
int index = row * rows + col;
int transposedIndex = col * rows + row;
if (col < cols && row < rows)
{
B[index] = A[transposedIndex];
}
} |
18,728 | #include "includes.h"
//============================================================================
// Name : CudaMap.cu
// Author : Hang
//============================================================================
using namespace std;
__global__ void addTen(float* d, int count) {
int threadsPerBlock = blockDim.x * blockDim.y * blockDim.z;
// Thread position in the block
int threadPosInBlock = threadIdx.x + blockDim.x * threadIdx.y +
blockDim.x * blockDim.y * threadIdx.z;
// Block position in grid
int blockPosInGrid = blockIdx.x + gridDim.x * blockIdx.y +
gridDim.x * gridDim.y * blockIdx.z;
// Final thread ID
int tid = blockPosInGrid * threadsPerBlock + threadPosInBlock;
if (tid < count) {
d[tid] = d[tid] + 10;
}
} |
18,729 | /*
* Author Oleksandr Borysov
* Task3
*/
#include <stdio.h>
#include <stdlib.h>
#include <curand.h>
#include <curand_kernel.h>
#include <math.h>
#include <ctime>
#define MAX 32767
#define PLOT_DATA_FILE "plot_data3.txt"
#define SEED 254321
__global__ void getCounts(double* results, unsigned long* idxSteps, unsigned long* steps) {
double x, y, z;
unsigned long count = 0;
// index of thread
int idx = blockIdx.x * blockDim.x + threadIdx.x;
curandState_t state;
curand_init(SEED + idx, 0, 0, &state);
for (unsigned long i = 0; i < idxSteps[idx]; ++i) {
x = ((double)((curand(&state)) % MAX)) / MAX;
y = ((double)((curand(&state)) % MAX)) / MAX;
z = sqrt((x * x) + (y * y));
if (z <= 1) {
++count;
}
}
results[idx] = ((double)count / *steps) * 4.0;
}
int main(int argc, char* argv[]) {
unsigned long stepNumber, threadBlock, threads, threadSteps, threadNumber;
unsigned long *steps;
double *results;
cudaError_t cudaStatus;
clock_t begin = 0;
printf("Type number of steps \n");
scanf("%lu", &stepNumber);
printf("Thread blocks \n");
scanf("%lu", &threadBlock);
printf("Threads in block \n");
scanf("%lu", &threads);
// stepNumber = 1000000; threadBlock = 10; threads = 10;
begin = clock();
threadNumber = threadBlock * threads;
threadSteps = stepNumber / threadNumber;
results = (double*) calloc(threadNumber, sizeof(double));
steps = (unsigned long*) calloc(threadNumber, sizeof(long));
for (int i = 0; i < threadNumber - 1; ++i) {
steps[i] = threadSteps;
}
steps[threadNumber - 1] = stepNumber - threadSteps * (threadNumber - 1);
unsigned long *d_steps, *d_stepsNumber;
double *d_results;
//----------------
cudaStatus = cudaMalloc(&d_results, sizeof(double) * threadNumber);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMalloc d_results failed!");
goto Error;
}
cudaStatus = cudaMalloc(&d_steps, sizeof(long) * threadNumber);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMalloc d_steps failed!");
goto Error;
}
cudaStatus = cudaMalloc(&d_stepsNumber, sizeof(long));
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMalloc d_stepsNumber failed!");
goto Error;
}
cudaStatus = cudaMemcpy(d_steps, steps, sizeof(long) * threadNumber, cudaMemcpyHostToDevice);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMemcpy d_steps failed!");
goto Error;
}
cudaStatus = cudaMemcpy(d_stepsNumber, &stepNumber, sizeof(long), cudaMemcpyHostToDevice);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMemcpy d_stepsNumber failed!");
goto Error;
}
// run CUDA method
getCounts <<<threadBlock, threads>>>(d_results, d_steps, d_stepsNumber);
cudaStatus = cudaMemcpy(results, d_results, sizeof(double) * threadNumber, cudaMemcpyDeviceToHost);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMemcpy d_result failed! code %d", cudaStatus);
goto Error;
}
Error:
cudaFree(d_results);
cudaFree(d_steps);
cudaFree(d_stepsNumber);
if (cudaStatus == 0) {
double time_spent = (double) (clock() - begin) / CLOCKS_PER_SEC;
double pi = 0;
for (unsigned long i = 0; i < threadNumber; ++i) {
pi += results[i];
}
printf("Calculated PI is = %f.\n Time= %f\n", pi, time_spent);
FILE* dataPlotFile;
dataPlotFile = fopen(PLOT_DATA_FILE, "a");
fprintf(dataPlotFile, "%d %f %d\n", threadNumber, time_spent, stepNumber);
fclose(dataPlotFile);
}
free(results);
free(steps);
return cudaStatus;
} |
18,730 | #include <stdio.h>
#include <cuda_runtime_api.h>
__global__ void empty()
{
return;
}
int main()
{
dim3 gridSize = dim3(1, 1, 1);
dim3 blockSize = dim3(1, 1, 1);
empty<<<gridSize, blockSize>>>();
printf("Hello World\n");
return 0;
}
|
18,731 | #include "includes.h"
using namespace std;
__global__ void propagateCarries(int* d_matrix, int numCols) {
int idx = blockDim.x * blockIdx.x + threadIdx.x * numCols;
int carry = 0;
for (int i = numCols - 1; i >= 0; i--) {
int rowVal = (d_matrix[idx + i] + carry) % 10;
carry = (d_matrix[idx + i] + carry) / 10;
d_matrix[idx + i] = rowVal;
}
} |
18,732 | #include <stdlib.h>
#include <math.h>
#include <string.h>
#include <stdio.h>
//#include "cg_cpu.h"
/*
The following functions implement the conjugate gradient algorithm
on the CPU as a pretest for the implementation on the GPU.
Uses float as this will also be fastest on the GPU.
*/
void set_zero(float * v, int n) {
for (int i=0; i<n; i++) {
v[i] = 0.0f;
}
}
void scalar_vec_add(float * res, float * v, float * w, float s, int n) {
/* add two arrays length n, with scalar multiplication as res = v + s*w */
for (int i=0; i<n; i++) {
res[i] = v[i] + s*w[i];
}
}
float scalar_prod(float * v, float * w, int n) {
/* returns scalar product of two arrays v, w*/
float res = 0;
for (int i=0; i<n; i++) {
res += v[i] * w[i];
}
return res;
}
float abs_vec(float * v, int n) {
/* returns the abs of a vector*/
return sqrt(scalar_prod(v, v, n));
}
void mat_vec_mul(float * res, float * mat, float * v, int n) {
/* matrix-vector multiplication for square matrices size n*n */
for (int i=0; i<n; i++) { // row index
res[i] = 0.0f;
for (int j=0; j<n; j++) { // column index
res[i] += mat[i*n + j]*v[j];
}
}
}
void cg(float * x, float * A, float * b, int n, int max_iter, float prec) {
/* conjugate gradient A*x = b (A=mat) */
float * r = (float*)malloc(n * sizeof(float)); // residue
float * p = (float*)malloc(n * sizeof(float)); // orthogonal search directions
float * ap = (float*)malloc(n * sizeof(float)); // A*p storage vector
float alpha = 0.0f;
float beta = 0.0f;
float rr = 0.0f; // <r,r> dot product
float rr_n = 0.0f; // <r,r> dot product for next iteration
float err;
float err_0;
int k = 0; // current iteration
// initialize starting values
mat_vec_mul(p, A, x, n); // p=A*x temp storage of A*x
scalar_vec_add(r, b, p, -1.0f, n); // r = b + -1*p
memcpy(p, r, n*sizeof(float));
rr = scalar_prod(r, r, n);
err = rr;
err_0 = err;
while((k < max_iter) && (err > prec*prec*err_0)) {
mat_vec_mul(ap, A, p, n);
alpha = rr/scalar_prod(r, ap, n);
scalar_vec_add(x, x, p, alpha, n);
if (k%50 == 0) {
mat_vec_mul(ap, A, x, n);
scalar_vec_add(r, b, ap, -1.0f, n);
printf("Residual adjusted!\n");
} else {
scalar_vec_add(r, r, ap, -1.0f*alpha, n);
}
rr_n = scalar_prod(r, r, n);
beta = rr_n/rr;
scalar_vec_add(p, r, p, beta, n);
rr = rr_n;
err = rr;
printf("Iteration: %d\n", k);
printf("Precision: %e\n", err);
k++;
}
if (k >= max_iter) {
printf("Maximum number of iterations (%d) reached, aborting calculation at precision (relative error) %e\n", k, abs_vec(r,n));
}
free(r);
free(p);
free(ap);
}
/* following are functions to produce positive definite, symmetric matrices */
void random_arr(float * arr, int n) {
// fill an array with (semi) random floats in range (-0.5, 0,5)
for (int i=0; i<n; i++) {
arr[i] = (float)rand()/RAND_MAX - 0.5f;
}
}
void gen_mat(float * mat, int n) {
float * transp = (float*)malloc(n*n*sizeof(float));
random_arr(mat, n*n);
for (int i=0; i<n; i++) {
mat[i*n + i] = 0.0f;
}
for (int i=0; i<n; i++){
for (int j=0; j<n; j++){
transp[i*n+j] = mat[j*n+i];
}
}
scalar_vec_add(mat, transp, mat, 1.0f, n*n);
for (int i=0; i<n; i++){
mat[i*n+i] += n;
}
free(transp);
}
|
18,733 | #include <cuda_runtime.h>
#include <cstdlib>
#include <iostream>
#include <time.h>
#include "CudaPhysics.cuh"
#include "CudaKernels.cuh"
void ConstructMatrixOfInfluenceCoefficientsCuda(
const float *h_cp_x,
const float *h_cp_y,
const float *h_cp_z,
const float *h_n_x,
const float *h_n_y,
const float *h_n_z,
const float *h_vs_x,
const float *h_vs_y,
const float *h_vs_z,
const float *h_ve_x,
const float *h_ve_y,
const float *h_ve_z,
float *h_A, // Output influence coefficient matrix.
int noOfUnknownVortexStrengths, // This is basically the number of surface panels.
float RankineCoreRadius,
char rankineAlgorithmIndex,
int FrameNumber)
{
// DOM: Called from ManageCalculationOfMatrixOfCoefficients in ITPhysics.cpp.
cudaError_t err; // Error code to check return values for CUDA calls.
// DOM: Calculate the sizes of the arrays passed in to this function.
int totalNumberOfCudaComputations = noOfUnknownVortexStrengths*noOfUnknownVortexStrengths; // This is the number of elements in the A matrix, and accounts for the influence of each panel on each panel.
size_t sizeRowFloat = noOfUnknownVortexStrengths * sizeof(float); // Memory required for a row of floats.
size_t sizeMatrixFloat = totalNumberOfCudaComputations * sizeof(float); // Memory required for a matrix of floats.
int maxNoOfVortices = 4;
// ============================================================================
// Allocate the GPU memory.
// ============================================================================
// Colocation point coordinates.
float *d_cp_x = NULL;
err = cudaMalloc((void **)&d_cp_x, sizeRowFloat); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_cp_y = NULL;
err = cudaMalloc((void **)&d_cp_y, sizeRowFloat); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_cp_z = NULL;
err = cudaMalloc((void **)&d_cp_z, sizeRowFloat); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
// Panel Normals.
float *d_n_x = NULL;
err = cudaMalloc((void **)&d_n_x, sizeRowFloat); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_n_y = NULL;
err = cudaMalloc((void **)&d_n_y, sizeRowFloat); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_n_z = NULL;
err = cudaMalloc((void **)&d_n_z, sizeRowFloat); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
// Vortex end point coordinates.
float *d_vs_x = NULL;
err = cudaMalloc((void **)&d_vs_x, sizeRowFloat*maxNoOfVortices); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_vs_y = NULL;
err = cudaMalloc((void **)&d_vs_y, sizeRowFloat*maxNoOfVortices); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_vs_z = NULL;
err = cudaMalloc((void **)&d_vs_z, sizeRowFloat*maxNoOfVortices); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_ve_x = NULL;
err = cudaMalloc((void **)&d_ve_x, sizeRowFloat*maxNoOfVortices); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_ve_y = NULL;
err = cudaMalloc((void **)&d_ve_y, sizeRowFloat*maxNoOfVortices); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_ve_z = NULL;
err = cudaMalloc((void **)&d_ve_z, sizeRowFloat*maxNoOfVortices); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
// The memory for the square matrix of influence coefficient entries.
float *d_A = NULL;
err = cudaMalloc((void **)&d_A, sizeMatrixFloat); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
// ============================================================================
// Copy host memory to device memory.
// ============================================================================
err = cudaMemcpy(d_cp_x, h_cp_x, sizeRowFloat, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_cp_y, h_cp_y, sizeRowFloat, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_cp_z, h_cp_z, sizeRowFloat, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_n_x, h_n_x, sizeRowFloat, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_n_y, h_n_y, sizeRowFloat, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_n_z, h_n_z, sizeRowFloat, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_vs_x, h_vs_x, sizeRowFloat*maxNoOfVortices, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_vs_y, h_vs_y, sizeRowFloat*maxNoOfVortices, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_vs_z, h_vs_z, sizeRowFloat*maxNoOfVortices, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_ve_x, h_ve_x, sizeRowFloat*maxNoOfVortices, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_ve_y, h_ve_y, sizeRowFloat*maxNoOfVortices, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_ve_z, h_ve_z, sizeRowFloat*maxNoOfVortices, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_A, h_A, sizeMatrixFloat, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
// ============================================================================
// Call the Kernel.
// ============================================================================
int noOfElementsInEachThread = 1;
int totalNumberOfThreads = (totalNumberOfCudaComputations + noOfElementsInEachThread - 1) / noOfElementsInEachThread;
int threadsPerBlock = 256; // 256; // When running out of resources, try reduce the threadsPerBlock.
int totalNumberOfBlocks = (totalNumberOfThreads + threadsPerBlock - 1) / threadsPerBlock;
int noOfBlocksX = 64;
int noOfBlocksY = (totalNumberOfBlocks + noOfBlocksX - 1) / noOfBlocksX;
dim3 grid(noOfBlocksX, noOfBlocksY, 1);
dim3 block(threadsPerBlock, 1, 1);
// Call noOfBlocksX*noOfBlocksY*threadsPerBlock instances of the kernel.
kernelInfluenceCoefficient << <grid, block >> > (
d_cp_x,
d_cp_y,
d_cp_z,
d_n_x,
d_n_y,
d_n_z,
d_vs_x,
d_vs_y,
d_vs_z,
d_ve_x,
d_ve_y,
d_ve_z,
d_A,
noOfUnknownVortexStrengths,
rankineAlgorithmIndex,
RankineCoreRadius,
FrameNumber);
// Synchronize the CUDA kernels.
cudaDeviceSynchronize();
// Deal with any errors.
err = cudaGetLastError();
if (err != cudaSuccess)
{
std::cout << "Failed to launch kernelInfluenceCoefficient kernel (error code " << cudaGetErrorString(err) << ")" << std::endl;
exit(EXIT_FAILURE);
}
// ====================================================================================================
// Copy the coefficient vector back from the GPU device.
// Copy the device result vector in device memory to the host result vector in host memory.
// ====================================================================================================
err = cudaMemcpy(h_A, d_A, sizeMatrixFloat, cudaMemcpyDeviceToHost);
// ============================================================================
// Free the GPU memory.
// ============================================================================
err = cudaFree(d_cp_x); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_cp_y); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_cp_z); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_n_x); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_n_y); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_n_z); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_vs_x); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_vs_y); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_vs_z); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_ve_x); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_ve_y); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_ve_z); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_A); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
}
void ComputeVelocitiesForBatchOfPointsCuda(
const float *h_cp_x,
const float *h_cp_y,
const float *h_cp_z,
const float *h_vs_x,
const float *h_vs_y,
const float *h_vs_z,
const float *h_ve_x,
const float *h_ve_y,
const float *h_ve_z,
float *h_cp_vx,
float *h_cp_vy,
float *h_cp_vz,
const float *h_vorticities,
int noOfVorticesPerPanel,
int noOfSubjectPanels,
int noOfVelocityPredictions,
int rankineAlgorithmIndex)
{
// DOM: Error code to check return values for CUDA calls.
cudaError_t err;
// DOM: Calculate the sizes of the arrays passed in to this function.
size_t sizeSubjectPanelsFloat = noOfSubjectPanels * sizeof(float); // Memory required for a row of floats.
size_t sizeVelocityPredictionsFloat = noOfVelocityPredictions * sizeof(float); // Memory required for noOfVelocityPredictions floats.
// ============================================================================
// Allocate the GPU memory.
// ============================================================================
// Object point coordinates.
float *d_cp_x = NULL;
err = cudaMalloc((void **)&d_cp_x, sizeVelocityPredictionsFloat); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_cp_y = NULL;
err = cudaMalloc((void **)&d_cp_y, sizeVelocityPredictionsFloat); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_cp_z = NULL;
err = cudaMalloc((void **)&d_cp_z, sizeVelocityPredictionsFloat); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
// Vortex end point coordinates.
float *d_vs_x = NULL;
err = cudaMalloc((void **)&d_vs_x, sizeSubjectPanelsFloat*noOfVorticesPerPanel); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_vs_y = NULL;
err = cudaMalloc((void **)&d_vs_y, sizeSubjectPanelsFloat*noOfVorticesPerPanel); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_vs_z = NULL;
err = cudaMalloc((void **)&d_vs_z, sizeSubjectPanelsFloat*noOfVorticesPerPanel); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_ve_x = NULL;
err = cudaMalloc((void **)&d_ve_x, sizeSubjectPanelsFloat*noOfVorticesPerPanel); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_ve_y = NULL;
err = cudaMalloc((void **)&d_ve_y, sizeSubjectPanelsFloat*noOfVorticesPerPanel); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_ve_z = NULL;
err = cudaMalloc((void **)&d_ve_z, sizeSubjectPanelsFloat*noOfVorticesPerPanel); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
// The memory for the object point velocities.
float *d_cp_vx = NULL;
err = cudaMalloc((void **)&d_cp_vx, sizeVelocityPredictionsFloat); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_cp_vy = NULL;
err = cudaMalloc((void **)&d_cp_vy, sizeVelocityPredictionsFloat); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
float *d_cp_vz = NULL;
err = cudaMalloc((void **)&d_cp_vz, sizeVelocityPredictionsFloat); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
// The memory for the panel vorticities
float *d_vorticities = NULL;
err = cudaMalloc((void **)&d_vorticities, sizeSubjectPanelsFloat); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
// ============================================================================
// Copy host memory to device memory.
// ============================================================================
err = cudaMemcpy(d_cp_x, h_cp_x, sizeVelocityPredictionsFloat, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_cp_y, h_cp_y, sizeVelocityPredictionsFloat, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_cp_z, h_cp_z, sizeVelocityPredictionsFloat, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_vs_x, h_vs_x, sizeSubjectPanelsFloat*noOfVorticesPerPanel, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_vs_y, h_vs_y, sizeSubjectPanelsFloat*noOfVorticesPerPanel, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_vs_z, h_vs_z, sizeSubjectPanelsFloat*noOfVorticesPerPanel, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_ve_x, h_ve_x, sizeSubjectPanelsFloat*noOfVorticesPerPanel, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_ve_y, h_ve_y, sizeSubjectPanelsFloat*noOfVorticesPerPanel, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_ve_z, h_ve_z, sizeSubjectPanelsFloat*noOfVorticesPerPanel, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_cp_vx, h_cp_vx, sizeVelocityPredictionsFloat, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_cp_vy, h_cp_vy, sizeVelocityPredictionsFloat, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_cp_vz, h_cp_vz, sizeVelocityPredictionsFloat, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaMemcpy(d_vorticities, h_vorticities, sizeSubjectPanelsFloat, cudaMemcpyHostToDevice); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
// =========================================================================================
// Call the Kernel.
// =========================================================================================
int noOfElementsInEachThread = 1; // The number of object point array elements computed by each instance of the kernel function.
int totalNumberOfThreads = (noOfVelocityPredictions + noOfElementsInEachThread - 1) / noOfElementsInEachThread;
// TODO: Temporarily reduce threadsPerBlock to 1 to see if it helps with cuPrint.
int threadsPerBlock = 1; // 256; // When running out of resources, try reduce the threadsPerBlock.
int totalNumberOfBlocks = (totalNumberOfThreads + threadsPerBlock - 1) / threadsPerBlock;
int noOfBlocksX = 64;
int noOfBlocksY = (totalNumberOfBlocks + noOfBlocksX - 1) / noOfBlocksX;
dim3 grid(noOfBlocksX, noOfBlocksY, 1);
dim3 block(threadsPerBlock, 1, 1);
// Initialize tranche variables for tranche execution.
int threadsPerTranche = 6000;
int noOfTranches = (noOfVelocityPredictions + threadsPerTranche - 1) / threadsPerTranche;
for (int trancheIndex = 0; trancheIndex < noOfTranches; trancheIndex++)
{
clock_t time_end;
time_end = clock() + 10 * CLOCKS_PER_SEC / 1000;
while (clock() < time_end)
{
}
// Sort out tranche start index.
int indexOfStartOfTranche = trancheIndex*threadsPerTranche;
// Call noOfBlocksX*noOfBlocksY*threadsPerBlock instances of the kernel.
kernelFunctionPredictVelocityAtPoint << <grid, block >> > (
d_cp_x,
d_cp_y,
d_cp_z,
d_vs_x,
d_vs_y,
d_vs_z,
d_ve_x,
d_ve_y,
d_ve_z,
d_cp_vx,
d_cp_vy,
d_cp_vz,
d_vorticities,
noOfVorticesPerPanel,
noOfSubjectPanels,
noOfElementsInEachThread, // Usually set to 1.
noOfVelocityPredictions,
threadsPerTranche,
indexOfStartOfTranche,
rankineAlgorithmIndex);
cudaDeviceSynchronize();
// Deal with any errors.
err = cudaGetLastError();
if (err != cudaSuccess)
{
std::cout << "Failed to launch kernelFunctionPredictVelocityAtPoint kernel (error code " << cudaGetErrorString(err) << ")" << std::endl;
exit(EXIT_FAILURE);
}
} // End of for tranches.
err = cudaMemcpy(h_cp_vx, d_cp_vx, sizeVelocityPredictionsFloat, cudaMemcpyDeviceToHost);
err = cudaMemcpy(h_cp_vy, d_cp_vy, sizeVelocityPredictionsFloat, cudaMemcpyDeviceToHost);
err = cudaMemcpy(h_cp_vz, d_cp_vz, sizeVelocityPredictionsFloat, cudaMemcpyDeviceToHost);
// Free the GPU memory.
err = cudaFree(d_cp_x); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_cp_y); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_cp_z); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_vs_x); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_vs_y); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_vs_z); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_ve_x); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_ve_y); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_ve_z); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_cp_vx); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_cp_vy); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_cp_vz); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
err = cudaFree(d_vorticities); if (err != cudaSuccess) { exit(EXIT_FAILURE); }
} // End of ComputeVelocitiesForBatchOfPointsCuda. |
18,734 | #include <stdio.h>
#include <stdlib.h>
#include <cuda_runtime.h>
#include <time.h>
#include <stdio.h>
#include "device_launch_parameters.h"
#define DATA_SIZE 1048576
#define BLOCK_NUM 32
#define THREAD_NUM 256
int data[DATA_SIZE];
clock_t clockBegin, clockEnd;
__global__ static void sumOfSquares(int *num, int* result,
clock_t* time)
{
const int tid = threadIdx.x;
const int bid = blockIdx.x;
int sum = 0;
int i;
if(tid == 0) time[bid] = clock();
for(i = bid * THREAD_NUM + tid; i < DATA_SIZE;
i += BLOCK_NUM * THREAD_NUM) {
sum += num[i] * num[i];
}
result[bid * THREAD_NUM + tid] = sum;
if(tid == 0) time[bid + BLOCK_NUM] = clock();
}
void PrintfContainerElapseTime(char *pszContainerName, char *pszOperator, long lElapsetime)
{
printf("%s %s time %dsec\n", pszContainerName, pszOperator, lElapsetime);
}
void GenerateNumbers(int *number, int size)
{
for(int i = 0; i < size; i++) {
number[i] = rand() % 10;
}
}
bool InitCUDA()
{
int count;
cudaGetDeviceCount(&count);
if(count == 0) {
fprintf(stderr, "There is no device./n");
return false;
}
int i;
for(i = 0; i < count; i++) {
cudaDeviceProp prop;
if(cudaGetDeviceProperties(&prop, i) == cudaSuccess) {
if(prop.major >= 1) {
break;
}
}
}
if(i == count) {
fprintf(stderr, "There is no device supporting CUDA 1.x./n");
return false;
}
cudaSetDevice(i);
return true;
}
int main()
{
if(!InitCUDA()) {
return 0;
}
printf("CUDA initialized.\n");
GenerateNumbers(data, DATA_SIZE);
/*int* gpudata, *result;
cudaMalloc((void**) &gpudata, sizeof(int) * DATA_SIZE);
cudaMalloc((void**) &result, sizeof(int));
cudaMemcpy(gpudata, data, sizeof(int) * DATA_SIZE,
cudaMemcpyHostToDevice);
sumOfSquares<<<1, 1, 0>>>(gpudata, result);
int sum;
cudaMemcpy(&sum, result, sizeof(int), cudaMemcpyDeviceToHost);
cudaFree(gpudata);
cudaFree(result);
printf("sum: %d\n", sum);*/
int* gpudata, *result;
clock_t* time;
cudaMalloc((void**) &gpudata, sizeof(int) * DATA_SIZE);
cudaMalloc((void**) &result,
sizeof(int) * THREAD_NUM * BLOCK_NUM);
cudaMalloc((void**) &time, sizeof(clock_t) * BLOCK_NUM * 2);
cudaMemcpy(gpudata, data, sizeof(int) * DATA_SIZE,
cudaMemcpyHostToDevice);
sumOfSquares<<<BLOCK_NUM, THREAD_NUM, 0>>>(gpudata, result,
time);
int sum[THREAD_NUM * BLOCK_NUM];
clock_t time_used[BLOCK_NUM * 2];
cudaMemcpy(&sum, result, sizeof(int) * THREAD_NUM * BLOCK_NUM,
cudaMemcpyDeviceToHost);
cudaMemcpy(&time_used, time, sizeof(clock_t) * BLOCK_NUM * 2,
cudaMemcpyDeviceToHost);
cudaFree(gpudata);
cudaFree(result);
cudaFree(time);
int final_sum = 0;
for(int i = 0; i < THREAD_NUM * BLOCK_NUM; i++) {
final_sum += sum[i];
}
clock_t min_start, max_end;
min_start = time_used[0];
max_end = time_used[BLOCK_NUM];
for(int i = 1; i < BLOCK_NUM; i++) {
if(min_start > time_used[i])
min_start = time_used[i];
if(max_end < time_used[i + BLOCK_NUM])
max_end = time_used[i + BLOCK_NUM];
}
printf("sum: %d time: %d\n", final_sum, max_end - min_start);
final_sum = 0;
for(int i = 0; i < THREAD_NUM; i++) {
final_sum += sum[i];
}
printf("sum: %d time: %d\n", final_sum, time_used);
final_sum = 0;
for(int i = 0; i < DATA_SIZE; i++) {
final_sum += data[i] * data[i];
}
printf("sum (CPU): %d\n", final_sum);
getchar();
return 0;
}
|
18,735 | #include "includes.h"
#define INF 2147483647
extern "C" {
}
__global__ void oneReduction(int * tab, int len, int mod) {
__shared__ int begin, end;
__shared__ int tmp_T[1024];
if(threadIdx.x == 0) {
begin = blockIdx.x*len;
end = blockIdx.x*len + len;
}
__syncthreads();
if(blockIdx.x % mod < mod/2) {
for(int k = len/2; k >= 1024; k /= 2) {
for(int g = begin; g < end; g += 2*k) {
for(int j = g; j < g + k; j += 512) {
__syncthreads();
if(threadIdx.x < 512)
tmp_T[threadIdx.x] = tab[j + threadIdx.x];
else
tmp_T[threadIdx.x] = tab[j + threadIdx.x - 512 + k];
__syncthreads();
if(threadIdx.x < 512 && tmp_T[threadIdx.x] > tmp_T[threadIdx.x + 512]) {
tmp_T[threadIdx.x] ^= tmp_T[threadIdx.x + 512];
tmp_T[threadIdx.x + 512] ^= tmp_T[threadIdx.x];
tmp_T[threadIdx.x] ^= tmp_T[threadIdx.x + 512];
}
__syncthreads();
if(threadIdx.x < 512)
tab[j + threadIdx.x] = tmp_T[threadIdx.x];
else
tab[j + threadIdx.x - 512 + k] = tmp_T[threadIdx.x];
}
}
}
for(int i = begin; i < begin+len; i += 1024) {
__syncthreads();
tmp_T[threadIdx.x] = tab[i + threadIdx.x];
__syncthreads();
for(int jump = 512; jump >= 1; jump /= 2) {
if(threadIdx.x % (jump*2) < jump && threadIdx.x + jump < 1024 && tmp_T[threadIdx.x] > tmp_T[threadIdx.x + jump]) {
tmp_T[threadIdx.x] ^= tmp_T[threadIdx.x + jump];
tmp_T[threadIdx.x + jump] ^= tmp_T[threadIdx.x];
tmp_T[threadIdx.x] ^= tmp_T[threadIdx.x + jump];
}
__syncthreads();
}
tab[i + threadIdx.x] = tmp_T[threadIdx.x];
}
} else {
for(int k = len/2; k >= 1024; k /= 2) {
for(int g = begin; g < end; g += 2*k) {
for(int j = g; j < g + k; j += 512) {
__syncthreads();
if(threadIdx.x < 512)
tmp_T[threadIdx.x] = tab[j + threadIdx.x];
else
tmp_T[threadIdx.x] = tab[j + threadIdx.x - 512 + k];
__syncthreads();
if(threadIdx.x < 512 && tmp_T[threadIdx.x] < tmp_T[threadIdx.x + 512]) {
tmp_T[threadIdx.x] ^= tmp_T[threadIdx.x + 512];
tmp_T[threadIdx.x + 512] ^= tmp_T[threadIdx.x];
tmp_T[threadIdx.x] ^= tmp_T[threadIdx.x + 512];
}
__syncthreads();
if(threadIdx.x < 512)
tab[j + threadIdx.x] = tmp_T[threadIdx.x];
else
tab[j + threadIdx.x - 512 + k] = tmp_T[threadIdx.x];
}
}
}
for(int i = begin; i < begin + len; i += 1024) {
__syncthreads();
tmp_T[threadIdx.x] = tab[i + threadIdx.x];
__syncthreads();
for(int jump = 512; jump >= 1; jump /= 2) {
if(threadIdx.x % (jump*2) < jump && threadIdx.x + jump < 1024 && tmp_T[threadIdx.x] < tmp_T[threadIdx.x + jump]) {
tmp_T[threadIdx.x] ^= tmp_T[threadIdx.x + jump];
tmp_T[threadIdx.x + jump] ^= tmp_T[threadIdx.x];
tmp_T[threadIdx.x] ^= tmp_T[threadIdx.x + jump];
}
__syncthreads();
}
tab[i + threadIdx.x] = tmp_T[threadIdx.x];
}
}
} |
18,736 | #include "includes.h"
/*
Now we make the matrix much bigger
g++ -pg seq_matrix_big_mul.c -o seq_matrix_big_mul
*/
#define N_THREADS 32
int num_rows_A = 2000; int num_rows_B = 2000; int num_rows_C = 2000;
int num_cols_A = 2000; int num_cols_B = 600; int num_cols_C = 600;
//int num_rows_A = 64; int num_rows_B = 64; int num_rows_C = 64;
//int num_cols_A = 64; int num_cols_B = 64; int num_cols_C = 64;
// I'm forcing a malloc because I want to add the malloc time on the game
float *A = (float*) malloc(sizeof(float) * num_rows_A * num_cols_A);
float *B = (float*) malloc(sizeof(float) * num_rows_B * num_cols_B);
float *C = (float*) malloc(sizeof(float) * num_rows_C * num_cols_C);
float *C_ref = (float*) malloc(sizeof(float) * num_rows_C * num_cols_C);
__global__ void matrix_2d_mul_float_gpu(float *A, float *B, float *C, int num_rows_A, int num_cols_A, int num_cols_B) {
// Same code for all 2d kernel
int i = blockIdx.y * blockDim.y + threadIdx.y;
int k = blockIdx.x * blockDim.x + threadIdx.x;
if (i > num_rows_A || k > num_cols_B) return;
float sum = 0;
for (int j=0; j<num_cols_A; j++){
// A[i][j] == A[i*num_cols_A+j]
// B[j][k] == B[j*num_cols_B+k]
//sum += A[i][j]*B[j][k];
sum += A[i*num_cols_A+j]*B[j*num_cols_B+k];
}
C[i*num_cols_B+k]=sum;
} |
18,737 | //#include <stdio.h>
//#include <stdlib.h>
//
//#include "cuda_runtime.h"
//#include "device_launch_parameters.h"
//#include "common.h"
//#include "cuda_common.cuh"
//
//__global__ void k1()
//{
// int gid = blockDim.x * blockIdx.x + threadIdx.x;
// if (gid == 0)
// {
// printf("This is a test 1 \n");
// }
//}
//
//__global__ void k2()
//{
// int gid = blockDim.x * blockIdx.x + threadIdx.x;
// if (gid == 0)
// {
// printf("This is a test 2 \n");
// }
//}
//
//__global__ void k3()
//{
// int gid = blockDim.x * blockIdx.x + threadIdx.x;
// if (gid == 0)
// {
// printf("This is a test 3 \n");
// }
//}
//int main(int argc, char ** argv)
//{
// int size = 1 << 15;
// cudaEvent_t event_str1;
// gpuErrchk(cudaEventCreateWithFlags(&event_str1,cudaEventDisableTiming));
//
// cudaStream_t stm1,stm2,stm3;
// gpuErrchk(cudaStreamCreate(&stm1));
// gpuErrchk(cudaStreamCreate(&stm2));
// gpuErrchk(cudaStreamCreate(&stm3));
//
// dim3 block(128);
// dim3 grid(size / block.x);
//
// k1 << <grid, block, 0, stm1 >> > ();
// cudaEventRecord(event_str1, stm1);
// cudaStreamWaitEvent(stm3, event_str1,0);
//
// k2 << <grid, block, 0, stm2 >> > ();
// k3 << <grid, block, 0, stm3 >> > ();
//
// gpuErrchk(cudaEventDestroy(event_str1));
//
// gpuErrchk(cudaStreamDestroy(stm1));
// gpuErrchk(cudaStreamDestroy(stm2));
// gpuErrchk(cudaStreamDestroy(stm3));
// gpuErrchk(cudaDeviceSynchronize());
//
// gpuErrchk(cudaDeviceReset());
// return 0;
//}
|
18,738 | #include <stdio.h>
#include <stdlib.h>
#include <math.h>
#define INF 1073741824
#define BLOCK_SZ 16
int m; // nodes
int n; // dimensions
int k; // k-nearest
// input sample file
int* load(const char *input)
{
FILE *file = fopen(input, "r");
if (!file) {
fprintf(stderr, "Error: no such input file \"%s\"\n", input);
exit(1);
}
// load m, n, k
fscanf(file, "%d%d%d", &m, &n, &k);
// allocate memory
int *data = (int*)malloc(sizeof(int) * m * n);
// load data
for (int i = 0; i < m * n; i++) {
fscanf(file, "%d", data + i);
}
fclose(file);
return data;
}
__global__ void distances(int *data, int *dis, int m, int n)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
int j = blockDim.y * blockIdx.y + threadIdx.y;
if (i > j || i >= m || j >= m) return;
if (i == j) {
dis[i * m + i] = INF;
} else {
int tmp1;
int tmp2 = 0;
for (int l = 0; l < n; l++) { // for each dimension
tmp1 = data[i * n + l] - data[j * n + l];
tmp2 += tmp1 * tmp1;
}
dis[i * m + j] = dis[j * m + i] = tmp2;
}
}
__global__ void sort(int *dis, int *result, int m, int k)
{
int i = blockDim.x * blockIdx.x + threadIdx.x;
if (i >= m) return;
int tmp, idx;
for (int j = 0; j < k; j++) { // find j-th nearest neighbor
tmp = INF;
for (int l = i * m; l < (i + 1) * m; l++) {
if (dis[l] < tmp) {
tmp = dis[l];
idx = l;
}
}
result[i * k + j] = idx % m;
dis[idx] = INF;
}
}
void knn(int *data, int *result)
{
int *d_data, *d_result, *d_dis;
int block = ceil(m / (double)BLOCK_SZ);
float timer1, timer2;
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaMalloc((void**)&d_data, sizeof(int) * m * n);
cudaMalloc((void**)&d_result, sizeof(int) * m * k);
cudaMalloc((void**)&d_dis, sizeof(int) * m * m);
cudaMemcpy(d_data, data, sizeof(int) * m * n, cudaMemcpyHostToDevice);
cudaEventRecord(start);
distances<<<dim3(block, block, 1), dim3(BLOCK_SZ, BLOCK_SZ, 1)>>>(d_data, d_dis, m, n);
cudaEventRecord(stop);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&timer1, start, stop);
cudaEventRecord(start);
sort<<<block, BLOCK_SZ>>>(d_dis, d_result, m, k);
cudaEventRecord(stop);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&timer2, start, stop);
cudaMemcpy(result, d_result, sizeof(int) * m * k, cudaMemcpyDeviceToHost);
fprintf(stderr, "distance: %.4lf ms\n", timer1);
fprintf(stderr, "sort: %.4lf ms\n", timer2);
cudaFree(d_data);
cudaFree(d_result);
cudaFree(d_dis);
cudaEventDestroy(start);
cudaEventDestroy(stop);
}
int main(int argc, char **argv)
{
if (argc != 2) {
fprintf(stderr, "Usage: %s input_file\n", argv[0]);
exit(1);
}
// input
int *data = load(argv[1]);
int *result = (int*)malloc(sizeof(int) * m * k);
// compute
knn(data, result);
// output
for (int i = 0; i < m; i++) {
for (int j = 0; j < k; j++) {
printf("%d ", result[i * k + j]);
}
printf("\n");
}
free(data);
free(result);
return 0;
}
|
18,739 | //#include "kernel.cuh"
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <math.h>
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#define N 5000
__host__
bool checkArr(int *arr, int size)
{
for (int i = 0; i < size-1; ++i)
{
if (arr[i] > arr[i + 1])
{
printf("Array index: %d, with value: %d\nIs greater than index: %d, with value: %d\n", i, arr[i], i + 1, arr[i + 1]);
}
}
return true;
}
__host__
void printArr(int *arr, int size)
{
for (int i = 0; i < size - 1; ++i)
{
printf("%d, ", arr[i]);
}
printf("%d \n\n", arr[size - 1]);
}
__host__
void createRandArr(int *arr, int size, int maxVal)
{
for (int i = 0; i < size; ++i)
arr[i] = (rand() / (float)(RAND_MAX)) * maxVal;
}
__host__
int oddeven(int *arr, int size, int oddeven)
{
int sorted = 0;
for (int i = oddeven; i < size-oddeven; i += 2)
{
int minStep = arr[i] > arr[i + 1];
int min = arr[i + minStep];
int maxStep = arr[i] <= arr[i + 1];
int max = arr[i + maxStep];
arr[i] = min;
arr[i + 1] = max;
sorted += minStep - maxStep;
}
return sorted;
}
__host__
void sortCPU(int *arr, int size)
{
int i = 0;
int sorted = 1;
while (sorted != (-size+1))
{
sorted = oddeven(arr, size, i % 2);
sorted += oddeven(arr, size, (i+1) % 2);
i += 2;
}
}
//__device__
//int oddevenGPU(int *d_arr, int size, int oddeven, int blockSize, int startIndex, int endIndex)
//{
// int sorted = 0;
// for (int i = startIndex; i < endIndex; i += 2)
// {
// int minStep = d_arr[i] > d_arr[i + 1];
// int min = d_arr[i + minStep];
// int maxStep = d_arr[i] <= d_arr[i + 1];
// int max = d_arr[i + maxStep];
//
// d_arr[i] = min;
// d_arr[i + 1] = max;
//
// sorted += minStep - maxStep;
// }
// return sorted;
//}
//
//__global__
//void addKernel(int *d_arr, int *d_size, int *d_blockSize, int *d_sorted)
//{
// int size = *d_size;
// int blockSize = *d_blockSize;
// int nrThreads = size / blockSize;
// int elemInThread = size / nrThreads;
// int shift = elemInThread % 2;
//
// int i = 0;
// int sorted = 0;
// int oddeven = 0;
// while (sorted != (-size + 1))
// {
// sorted = 0;
//
//
// oddeven = i % 2; //0 == odd, 1 == even
// int startIndex = blockSize * threadIdx.x + oddeven + (shift * ((threadIdx.x + 1) % 2) * threadIdx.x != 0);
// int endIndex = blockSize + blockSize * threadIdx.x - oddeven + shift * ((threadIdx.x + 1)%2);
//
// sorted += oddevenGPU(d_arr, size, oddeven, blockSize, startIndex, endIndex);
// __syncthreads();
//
//
// oddeven = (i + 1) % 2;
// startIndex = blockSize * threadIdx.x + oddeven;
// endIndex = blockSize + blockSize * threadIdx.x - oddeven;
//
// sorted += oddevenGPU(d_arr, size, oddeven, blockSize, startIndex, endIndex);
// __syncthreads();
// i += 2;
// }
//}
//int main()
//{
// srand((unsigned int)time(NULL));
//
// int size = 100;
// int *arr = (int*)malloc(size * sizeof(int));
// int *d_arr, *d_size, *d_blockSize, *d_sorted;
// createRandArr(arr, size, size*2);
//
// int n = 2;
// int blockSize = size / n;
//
// printArr(arr, size);
//
// cudaMalloc(&d_arr, size * sizeof(int));
// cudaMalloc(&d_size, sizeof(int));
// cudaMalloc(&d_blockSize, sizeof(int));
// cudaMalloc(&d_sorted, sizeof(int));
//
// cudaMemcpy(d_arr, arr, size * sizeof(int), cudaMemcpyHostToDevice);
// cudaMemcpy(d_size, &size, sizeof(int), cudaMemcpyHostToDevice);
// cudaMemcpy(d_blockSize, &blockSize, sizeof(int), cudaMemcpyHostToDevice);
// cudaMemcpy(d_sorted, &size, sizeof(int), cudaMemcpyHostToDevice);
//
// int nr = size / blockSize;
//
// addKernel<<<1, (size/2)>>>(d_arr, d_size, d_blockSize, d_sorted);
// cudaMemcpy(arr, d_arr, size * sizeof(int), cudaMemcpyDeviceToHost);
//
// printArr(arr, size);
//
// /*printArr(arr, size);
// sortCPU(arr, size);
// printArr(arr, size);*/
//
// system("pause");
//
// cudaFree(d_arr);
// cudaFree(d_size);
// free(arr);
// return 0;
//}
__global__
void oddeven(int *arr, int flag, int nrThreads, int size)
{
int d_flag = flag%2;
int sizeNR = (size / nrThreads) + ((size / nrThreads) % 2);
int index = (blockIdx.x * blockDim.x + threadIdx.x) * sizeNR;
if ((index >= size - 1) && d_flag != 0) return; //Out of bounds
int end = sizeNR + (blockIdx.x * blockDim.x + threadIdx.x) * sizeNR;
if (end >= size -1 - d_flag)
end = size - 1 - d_flag;
index += d_flag;
for (int i = index; i < end; i += 2)
{
int min = arr[i + (arr[i] > arr[i + 1])];
int max = arr[i + (arr[i] <= arr[i + 1])];
arr[i] = min;
arr[i + 1] = max;
}
}
int main()
{
int *arr;
int *d_arr;
int i;
int size = sizeof(int) * N;
srand((unsigned)time(NULL));
arr = (int*)malloc(size);
cudaMalloc(&d_arr, size);
createRandArr(arr, N, N * 2);
//printArr(arr, N);
cudaMemcpy(d_arr, arr, size, cudaMemcpyHostToDevice);
double start_time = clock();
for (i = 0; i < N; ++i)
{
oddeven<<<1, 500>>>(d_arr, i, 500, N);
}
printf("\nExecution time: %lf seconds.\n", (clock() - start_time) / CLOCKS_PER_SEC);
cudaMemcpy(arr, d_arr, size, cudaMemcpyDeviceToHost);
//printArr(arr, N);
bool sorted = checkArr(arr, N);
system("pause");
return 0;
} |
18,740 | #include <stdio.h>
__global__ void transpose(unsigned char *odata, const unsigned char *idata)
{
int H = blockDim.x * gridDim.x; // # dst_height
int W = blockDim.y * gridDim.y; // # dst_width
int h = blockDim.x * blockIdx.x + threadIdx.x; // 32 * bkIdx[0:18] + tdIdx; [0,607] # x / h-th row
int w = blockDim.y * blockIdx.y + threadIdx.y; // 32 * bkIdx[0:18] + tdIdx; [0,607] # y / w-th col
int C = 3; // # ChannelDim
int c = blockIdx.z % 3 ; // [0,2] # ChannelIdx
int n = blockIdx.z / 3 ; // [0 , Batch size-1], # BatchIdx
long src_idx = n * (H * W * C) +
h * (W * C) +
w * C +
c;
long dst_idx = n * (C * H * W) +
c * (H * W)+
h * W+
w;
odata[dst_idx] = idata[src_idx];
}
int main(){
// dim3 dimBlock(32,32,1); << Max total is 1024 , so , x=32 ,y=32 , some one use 1024 to handle flatten tensor is fine.
// dim3 dimGrid(19,19,3); << x = 608 / 32 = 19 , same on y , z = channel * batch_size, assume channel = 3.
dim3 dimBlock(32,32,1);
dim3 dimGrid(19,19,3);
// init host array
unsigned char host_src[608*608*3]; // N H W C
// unsigned char host_dst[1108992];
unsigned char host_dst[608*608*3]; // N C H W
// init src image
for(int i = 0; i < 608*608*3; i++){
// host_src[i] = i+1;
host_src[i] = (i%3);
}
// init device array
unsigned char *device_src, *device_dst;
cudaMalloc((unsigned char **)&device_src, 608*608*3* sizeof(unsigned char));
cudaMalloc((unsigned char **)&device_dst, 608*608*3* sizeof(unsigned char));
cudaMemcpy(device_src , host_src , 608*608*3 * sizeof(unsigned char), cudaMemcpyHostToDevice);
// run kernel
transpose<<<dimGrid, dimBlock>>>(device_dst, device_src);
cudaDeviceSynchronize();
// take out output
cudaMemcpy(host_dst, device_dst, 608*608*3 * sizeof(unsigned char), cudaMemcpyDeviceToHost);
// DEBUG : print first image in batch , first 30 pixel in 3 channels.
for(int i = 0; i < 30*3; i+=3){ // N H W C
printf("%d\n",host_src[i]);
}
printf("============================\n");
for(int c = 0; c<3*608*608 ; c+=608*608){ // N C H W
for(int i = 0 ; i < 30; i++){
printf("%d %d %d\n", c+i, i, host_dst[c+i]);
}
printf("------------------------------\n");
}
// deinit GPU
cudaFree(device_src);
cudaFree(device_dst);
return 0;
}
// clear && clear && nvcc NHWC2NCHW.cu -o trans.o && ./trans.o
|
18,741 | #include <cstdio>
int main() {
printf ("Hello, CUDA!\n");
return 0;
}
|
18,742 | #include <cstdio>
#include <cuda_runtime.h>
#include "main.cuh"
#define gpuCheck(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true)
{
if (code != cudaSuccess)
{
fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
if (abort) exit(code);
}
}
namespace {
constexpr size_t tile = 16;
__global__
void matmulV1(const float* a, const float* b, float* c, int i, int j, int k) {
// Figure out the output element I am writing to
int col = blockIdx.x * blockDim.x + threadIdx.x;
int row = blockIdx.y * blockDim.y + threadIdx.y;
if (row > i || col > k) {
return;
}
float dotp = 0.0;
for (auto idx = 0U ; idx < j ; ++idx) {
dotp += (a[row * j + idx] * b[col + idx * j]);
}
c[row * k + col] = dotp;
}
__global__
void matmulV2(const float* a, const float* b, float* c, int i, int j, int k) {
__shared__ float rowa[tile][tile];
__shared__ float cola[tile][tile];
// Figure out the output element I am writing to
// For this to work blockDim.x == tile, blockDim.y = tile
const int col = blockIdx.x * tile + threadIdx.x;
const int row = blockIdx.y * tile + threadIdx.y;
if (row > i || col > k) {
return;
}
const int tx = threadIdx.x;
const int ty = threadIdx.y;
float dotp = 0.0;
for (auto idx = 0 ; idx < j ; idx += tile) {
if ((idx + tx) < j)
rowa[ty][tx] = a[row * j + idx + tx];
if ((idx + ty < j))
cola[ty][tx] = b[col + (idx + ty) * j];
__syncthreads();
for (auto i = 0U ; i < tile ; ++i) {
// Don't add elements beyond the vector boundaries
dotp += rowa[ty][i] * cola[i][tx];
}
__syncthreads();
}
c[row * k + col] = dotp;
}
template <typename T>
void kernelRunner(const std::vector<float>& a,
const std::vector<float>& b,
std::vector<float>& c,
int i,
int j,
int k,
T fn) {
const auto a_n = a.size() * sizeof(float);
const auto b_n = b.size() * sizeof(float);
const auto c_n = c.size() * sizeof(float);
float* d_a;
float* d_b;
float* d_c;
gpuCheck(cudaMalloc(&d_a, a_n));
gpuCheck(cudaMalloc(&d_b, b_n));
gpuCheck(cudaMalloc(&d_c, c_n));
gpuCheck(cudaMemcpy(d_a, a.data(), a_n, cudaMemcpyHostToDevice));
gpuCheck(cudaMemcpy(d_b, b.data(), b_n, cudaMemcpyHostToDevice));
gpuCheck(cudaMemcpy(d_c, c.data(), c_n, cudaMemcpyHostToDevice));
dim3 dimGrid(ceil(i/(float)tile), ceil(k/(float)tile), 1);
dim3 dimBlock(tile, tile, 1);
printf("GridDim %d, %d, %d\n", dimGrid.x, dimGrid.y, dimGrid.z);
printf("BlockDim %d, %d, %d\n", dimBlock.x, dimBlock.y, dimBlock.z);
for (auto i = 0U ; i < 200 ; ++i) {
fn<<<dimGrid, dimBlock>>>(d_a, d_b, d_c, i, j, k);
gpuCheck(cudaPeekAtLastError());
cudaDeviceSynchronize();
}
gpuCheck(cudaMemcpy(c.data(), d_c, c_n, cudaMemcpyDeviceToHost));
gpuCheck(cudaFree(d_a));
gpuCheck(cudaFree(d_b));
gpuCheck(cudaFree(d_c));
}
} // unnamed namespace
namespace wrapper {
void matMulV1(const std::vector<float>& a,
const std::vector<float>& b,
std::vector<float>& c,
int i,
int j,
int k) {
kernelRunner(a, b, c, i, j, k, ::matmulV1);
}
// TODO: hook it up with the kernel that uses shared memory
void matMulV2(const std::vector<float>& a,
const std::vector<float>& b,
std::vector<float>& c,
int i,
int j,
int k) {
kernelRunner(a, b, c, i, j, k, ::matmulV2);
}
void print_cuda_properties() {
cudaDeviceProp props;
cudaGetDeviceProperties(&props, 0);
fprintf(stdout, "Warp size: %d\n", props.warpSize);
fprintf(stdout, "Max grid size: %d, %d, %d\n", props.maxGridSize[0], props.maxGridSize[1], props.maxGridSize[2]);
}
}
|
18,743 | #include "includes.h"
__global__ void profileLevelDown_kernel() {} |
18,744 | #include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/copy.h>
#include <thrust/fill.h>
#include <thrust/sequence.h>
#include <thrust/transform.h>
#include <thrust/replace.h>
#include <thrust/functional.h>
#include <thrust/sort.h>
#include <thrust/binary_search.h>
#include <thrust/random.h>
struct bloat_u8_5d_device {
__device__
uint64_t operator()(const uint8_t& x_) const {
uint64_t x = x_;
x |= x << 16;
x &= 0b0000000000000000111100000000000000001111;
x |= x << 8;
x |= x << 4;
x &= 0b0000100001000010000100001000010000100001;
return x;
}};
struct bloat_u8_6d_device {
__device__
uint64_t operator()(const uint8_t& x_) const {
uint64_t x = x_;
x |= x << 20;
x &= 0b000000000000000000001111000000000000000000001111;
x |= x << 10;
x |= x << 5;
x &= 0b000001000001000001000001000001000001000001000001;
return x;
}};
struct u8_5d_encoding_closure {
private:
unsigned int X, Y;
public:
template <typename Tuple>
__device__
void operator()(Tuple t)
{
uint64_t key = 0;
uint8_t r = thrust::get<0>(t);
uint8_t g = thrust::get<1>(t);
uint8_t b = thrust::get<2>(t);
unsigned int xy = thrust::get<3>(t);
uint64_t x_ = xy % X;
uint8_t x = x_ * 256 / X;
uint64_t y_ = xy / X;
uint8_t y = y_ * 256 / Y;
bloat_u8_5d_device bloater;
key |= bloater(x)<<4;
key |= bloater(y)<<3;
key |= bloater(r)<<2;
key |= bloater(g)<<1;
key |= bloater(b)<<0;
thrust::get<4>(t) = key;
}
u8_5d_encoding_closure(unsigned int X_, unsigned int Y_) {
X = X_;
Y = Y_;
}
};
struct u8_6d_encoding_closure {
private:
unsigned int X, Y, Z;
public:
template <typename Tuple>
__device__
void operator()(Tuple t)
{
uint64_t key = 0;
uint8_t r = thrust::get<0>(t);
uint8_t g = thrust::get<1>(t);
uint8_t b = thrust::get<2>(t);
unsigned int xyz = thrust::get<3>(t);
unsigned int xy = xyz / Z;
uint64_t x_ = xy % X;
uint8_t x = x_ * 256 / X;
uint64_t y_ = xy / X;
uint8_t y = y_ * 256 / Y;
uint64_t z_ = xyz % Z;
uint8_t z = z_ * 256 / Z;
bloat_u8_6d_device bloater;
key |= bloater(x)<<5;
key |= bloater(y)<<4;
key |= bloater(z)<<3;
key |= bloater(r)<<2;
key |= bloater(g)<<1;
key |= bloater(b)<<0;
thrust::get<4>(t) = key;
}
u8_6d_encoding_closure(unsigned int X_, unsigned int Y_, unsigned int Z_) {
X = X_;
Y = Y_;
Z = Z_;
}
};
struct clz_u8_5d_device {
__device__
uint64_t operator()(const uint64_t& x, const uint64_t& y) const { return (__clzll(x ^ y) - 24)/5; }};
struct clz_u8_6d_device {
__device__
uint64_t operator()(const uint64_t& x, const uint64_t& y) const { return (__clzll(x ^ y) - 16)/6; }};
thrust::host_vector<unsigned int> zbox_merge_rgb_image(
thrust::host_vector<uint8_t> Rs,
thrust::host_vector<uint8_t> Gs,
thrust::host_vector<uint8_t> Bs,
unsigned int X,
unsigned int Y)
{
using namespace thrust;
device_vector<uint8_t> Rs_dev = Rs;
device_vector<uint8_t> Gs_dev = Gs;
device_vector<uint8_t> Bs_dev = Bs;
unsigned int SIZE = X*Y;
device_vector<uint64_t> Keys(SIZE);
counting_iterator<unsigned int> XYs_first(0);
counting_iterator<unsigned int> XYs_last(SIZE);
for_each(
make_zip_iterator(make_tuple(Rs_dev.begin(), Gs_dev.begin(), Bs_dev.begin(), XYs_first, Keys.begin())),
make_zip_iterator(make_tuple(Rs_dev.end() , Gs_dev.end() , Bs_dev.end() , XYs_last , Keys.end())) ,
u8_5d_encoding_closure(X, Y)
);
sort(Keys.begin(), Keys.end());
transform(Keys.begin(), Keys.end() - 1, Keys.begin() + 1, (Rs_dev.begin() + 1), clz_u8_5d_device());
Rs_dev[0] = 0;
sort(Rs_dev.begin(), Rs_dev.end());
device_vector<unsigned int> histogram(9);
counting_iterator<unsigned int> search_begin(0);
upper_bound(Rs_dev.begin(), Rs_dev.end(),
search_begin, search_begin + 9,
histogram.begin());
host_vector<unsigned int> hoist(10, 1);
copy(histogram.begin(), histogram.end(), hoist.begin() + 1);
return hoist;
}
thrust::host_vector<unsigned int> zbox_merge_rgb_video(
thrust::host_vector<uint8_t> Rs,
thrust::host_vector<uint8_t> Gs,
thrust::host_vector<uint8_t> Bs,
unsigned int X,
unsigned int Y,
unsigned int Z)
{
using namespace thrust;
device_vector<uint8_t> Rs_dev = Rs;
device_vector<uint8_t> Gs_dev = Gs;
device_vector<uint8_t> Bs_dev = Bs;
unsigned int SIZE = X*Y*Z;
device_vector<uint64_t> Keys(SIZE);
counting_iterator<unsigned int> XYZs_first(0);
counting_iterator<unsigned int> XYZs_last(SIZE);
for_each(
make_zip_iterator(make_tuple(Rs_dev.begin(), Gs_dev.begin(), Bs_dev.begin(), XYZs_first, Keys.begin())),
make_zip_iterator(make_tuple(Rs_dev.end() , Gs_dev.end() , Bs_dev.end() , XYZs_last , Keys.end())) ,
u8_6d_encoding_closure(X, Y, Z)
);
sort(Keys.begin(), Keys.end());
transform(Keys.begin(), Keys.end() - 1, Keys.begin() + 1, (Rs_dev.begin() + 1), clz_u8_6d_device());
Rs_dev[0] = 0;
sort(Rs_dev.begin(), Rs_dev.end());
device_vector<unsigned int> histogram(9);
counting_iterator<unsigned int> search_begin(0);
upper_bound(Rs_dev.begin(), Rs_dev.end(),
search_begin, search_begin + 9,
histogram.begin());
host_vector<unsigned int> hoist(10, 1);
copy(histogram.begin(), histogram.end(), hoist.begin() + 1);
return hoist;
}
|
18,745 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <cstdlib>
#include <cstring>
#define BLOCK_SIZE 256
#define HISTOGRAM_LENGTH 256
__global__ void histo(unsigned char* buffer, unsigned int* histo, long size)
{
__shared__ unsigned int private_histo[256];
if(threadIdx.x < 256)
private_histo[threadIdx.x] = 0;
__syncthreads();
int i = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
while (i<size)
{
atomicAdd(&(private_histo[buffer[i]]),1);
i += stride;
}
__syncthreads();
if(threadIdx.x < 256)
atomicAdd(&(histo[threadIdx.x]), private_histo[threadIdx.x]);
}
int main()
{
unsigned char h_buffer[] = "AAAAAA bbbbbb ccccccccc abcdefg hijklmn HAHAHA wojiushi HAHAH AAAAAA bbbbbb ccccccccc abcdefg hijklmn HAHAHA wojiushi HAHAH";
unsigned int* h_histo;
unsigned char* d_buffer;
unsigned int* d_histo;
long size = strlen((char*)h_buffer);
h_histo = (unsigned int*)malloc(HISTOGRAM_LENGTH*sizeof(unsigned int));
cudaMalloc((void**)&d_buffer, size*sizeof(unsigned char));
cudaMalloc((void**)&d_histo, HISTOGRAM_LENGTH*sizeof(unsigned int));
cudaMemcpy(d_buffer, h_buffer, size*sizeof(unsigned char), cudaMemcpyHostToDevice);
dim3 dimBlock(BLOCK_SIZE,1,1);
dim3 dimGrid((size-1)/BLOCK_SIZE+1,1,1);
histo<<<dimGrid,dimBlock>>>(d_buffer, d_histo, size);
cudaMemcpy(h_histo,d_histo,HISTOGRAM_LENGTH*sizeof(unsigned int),cudaMemcpyDeviceToHost);
cudaFree(d_histo);
cudaFree(d_buffer);
printf("The result is:");
for(int i = 0; i< HISTOGRAM_LENGTH; ++i)
printf("%d,",h_histo[i]);
free(h_histo);
return 0;
} |
18,746 | __device__ float function_a_appli(float x);
__global__ void applique_fonc ( int nb_ligne, int nb_col, float * input, float * res ){
int index_col=threadIdx.x+blockDim.x*blockIdx.x;
int index_ligne=threadIdx.y+blockDim.y*blockIdx.y;
int global_index;
if ((index_col >= nb_col) || (index_ligne>=nb_ligne) ) return;
global_index=index_ligne*nb_col+index_col;
res[global_index]=function_a_appli(input[global_index]);
}
__device__ float function_a_appli(float x){
return( cos(x));
}
__global__ void mandelbrot ( int nb_ligne, int nb_col, float seuil, float x_min, float x_max, float y_min, float y_max, float* res) {
int max_ITER=10000;
int iter=0;
int index_col=threadIdx.x+blockDim.x*blockIdx.x;
int index_ligne=threadIdx.y+blockDim.y*blockIdx.y;
int global_index;
float x,y,xtemp,x0,y0;
if ((index_col >= nb_col) || (index_ligne>=nb_ligne) ) return;
global_index=index_ligne*nb_col+index_col;
x0=((float)index_col/(float)nb_col)*(x_max-x_min)+x_min;
y0=((float)(nb_ligne-index_ligne)/(float)nb_ligne)*(y_max-y_min)+y_min;
x=0;y=0;
while((x*x+y*y <= seuil) && (iter < max_ITER))
{ xtemp = x*x-y*y+x0;
y = 2*x*y+y0;
x = xtemp;
iter++;
}
res[global_index]=((float) iter/(float)max_ITER);
}
|
18,747 | #include <stdio.h>
#include <cuda.h>
__global__ void dkernel(unsigned *vector, unsigned vectorsize) {
unsigned id = blockIdx.x * blockDim.x + threadIdx.x;
vector[id] = id;
__syncthreads();
if (id < vectorsize - 1 && vector[id + 1] != id + 1) printf("syncthreads does not work.\n");
}
#define BLOCKSIZE 1000
#define N BLOCKSIZE
int main(int nn, char *str[]) {
unsigned *vector, *hvector;
cudaMalloc(&vector, N * sizeof(unsigned));
hvector = (unsigned *)malloc(N * sizeof(unsigned));
dkernel<<<100, BLOCKSIZE>>>(vector, N);
cudaMemcpy(hvector, vector, N * sizeof(unsigned), cudaMemcpyDeviceToHost);
for (unsigned ii = 0; ii < N; ++ii) {
printf("%4d ", hvector[ii]);
}
printf("\n");
return 0;
}
|
18,748 | //Transpuesta de una matriz
#include<iostream>
#include<stdio.h>
#include<malloc.h>
using namespace std;
__host__
void T(int *A, int filas, int columnas, int* B){
for(int j = 0; j < columnas; j++){
for(int i = 0; i < filas; i++){
B[j*filas+i] = A[i*columnas+j];
}
}
}
__global__
void TCU(int *A, int filas, int columnas, int* B){
int i = blockIdx.y*blockDim.y+threadIdx.y;//filas
int j = blockIdx.x*blockDim.x+threadIdx.x;//columnas
if(i < filas && j < columnas)
B[j*filas+i] = A[i*columnas+j];
}
__host__
void imprime(int* A,int filas, int columnas){//imprime como si fuera una matriz
for(int i = 0; i < filas; i++){
for(int j = 0; j < columnas; j++){
cout<<A[(i*columnas)+j]<<" ";
}
cout<<endl;
}
}
__host__
void inicializa(int *A,int filas, int columnas){//inicializa arreglos
for(int i=0;i<filas*columnas;i++){
A[i]=i;
}
}
__host__
bool compara(int *A, int *B, int filas, int columnas){
for(int i = 0; i < filas; i++){
for(int j = 0; j < columnas; j++){
if(A[i*columnas+j] != B[i*columnas+j]) return false;
}
}
return true;
}
int main(void){
cudaError_t error = cudaSuccess;//Para controlar errores
int *matriz, *Tmatriz, *h_matriz, *d_matriz, *d_Tmatriz;
int filas = 1024, columnas = 2048;
int size = filas*columnas*sizeof(int);
//----------------------CPU-------------------------
clock_t t = clock();//Iniciamos la cuenta de reloj
//Separamos memoria para el host
matriz = (int*)malloc(size);
Tmatriz = (int*)malloc(size);
//Inicializamos la matriz
inicializa(matriz, filas, columnas);
//Hacemos la transpuesta
T(matriz, filas, columnas, Tmatriz);
t = clock() - t;//Terminamos la cuenta de reloj
//Mostramos el resultado
/*
cout << "Original: " << endl;
imprime(matriz, filas, columnas);
cout << "transpuesta: " << endl;
imprime(Tmatriz, columnas, filas);
*/
double time_CPU = ((double)t) / CLOCKS_PER_SEC;
cout<<"El tiempo transcurrido en la CPU fue: "<<time_CPU<<endl;
//----------------------GPU--------------------------------------
h_matriz = (int*)malloc(size);//Este va a ser el resultado después de copiar los datos
//del device al host
t = clock();//Iniciamos la cuenta de reloj
//Separamos memoria para el device
error = cudaMalloc((void**)&d_matriz,size);
if(error != cudaSuccess){
cout<<"Error reservando memoria para d_matriz"<<endl;
//return -1;
}
cudaMalloc((void**)&d_Tmatriz,size);
if(error != cudaSuccess){
cout<<"Error reservando memoria para d_Tmatriz"<<endl;
//return -1;
}
//Copiamos datos del host al device
error = cudaMemcpy(d_matriz,matriz,size,cudaMemcpyHostToDevice);//destino d_matriz y origen matriz
if(error != cudaSuccess){
printf("Error copiando los datos de matriz a d_matriz \n");
//exit(-1);
}
//Lanzamos el kernel
dim3 dimblock(32,32,1);
//dim3 dimGrid(1,1,1);
dim3 dimGrid(ceil((double)(columnas/32)),ceil((double)(filas/32)),1);
TCU<<<dimGrid,dimblock>>>(d_matriz, filas, columnas, d_Tmatriz);
cudaDeviceSynchronize();
//Copiamos el resultado
error = cudaMemcpy(h_matriz,d_Tmatriz,size,cudaMemcpyDeviceToHost);
if(error != cudaSuccess){
printf("Error copiando los datos de d_Tmatriz a h_matriz \n");
//exit(-1);
}
t = clock() - t;//Terminamos la cuenta de reloj
double time_GPU = ((double)t) / CLOCKS_PER_SEC;
cout<<"El tiempo transcurrido en la GPU fue: "<<time_GPU<<endl;
//------------------------------------------------------------
cout<<"El tiempo de aceleramiento fue: "<<time_CPU/time_GPU<<endl;
if(compara(h_matriz, Tmatriz, filas, columnas)) cout << "Buen cálculo" << endl;
else cout << "Mal cálculo" << endl;
//Liberamos memoria
free(matriz); free(Tmatriz);
free(h_matriz);
cudaFree(d_matriz); cudaFree(d_Tmatriz);
return 0;
}
|
18,749 | #include <stdio.h>
#include <iostream>
#include <stdlib.h>
#include <assert.h>
#include <time.h>
#define R 3
#define BLOCK_SIZE 5 // number of output elements calculated in one block
int iDivUp(int a, int b){ return ((a % b) != 0) ? (a / b + 1) : (a / b); }
__global__ void oneD_stencil_shared(int *in_arr, int *out_arr, int n_input) {
// we declare a shared memory within a block, s.t. input elements don't have to be loaded from global memory several times
// reading from shared memory is faster!
__shared__ int temp_buffer[BLOCK_SIZE + 2*R];
int gindex = R + blockDim.x*blockIdx.x + threadIdx.x;
int lindex = R + threadIdx.x;
if(gindex < n_input){
temp_buffer[lindex] = in_arr[gindex];
if(threadIdx.x < R){
temp_buffer[lindex-R] = in_arr[gindex-R];
temp_buffer[lindex + BLOCK_SIZE] = in_arr[gindex + BLOCK_SIZE];
}
}
// until this point we want the shared buffer to be filled, we wait
__syncthreads();
int res = 0;
for(int i=-R; i<=R; ++i){
res += temp_buffer[lindex + i];
}
int oindex = gindex - R;
out_arr[oindex] = res;
}
int main(void) {
int device;
cudaGetDevice(&device);
struct cudaDeviceProp props;
cudaGetDeviceProperties(&props, device);
printf("Using %s.\n\n", props.name);
// host copies of input and output array
int arr_in[13] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13};
int in_elements = 13;
int in_size = in_elements*sizeof(int);
int *arr_out;
int out_elements = in_elements-(2*R+1)+1;
int out_size = out_elements*sizeof(int);
arr_out = (int *)malloc(out_size);
int *d_arr_in, *d_arr_out; // host copies of input and output array
// Allocate space for device copies of a, b, c
cudaMalloc((void **)&d_arr_in, in_size);
cudaMalloc((void **)&d_arr_out, out_size);
// Copy inputs to device
cudaMemcpy(d_arr_in, arr_in, in_size, cudaMemcpyHostToDevice);
// Launch stencil() kernel on GPU
int n_blocks = iDivUp(in_elements, BLOCK_SIZE);
int n_threads_per_block = BLOCK_SIZE;
oneD_stencil_shared<<<n_blocks, n_threads_per_block>>>(d_arr_in, d_arr_out, in_elements);
// Copy result back to host
cudaMemcpy(arr_out, d_arr_out, out_size, cudaMemcpyDeviceToHost);
std::cout << "[";
for(int i=0; i<out_elements; ++i){
std::cout << arr_out[i] << ", ";
}
std::cout << "]" << std::endl;
// Cleanup
cudaFree(d_arr_in); cudaFree(d_arr_out);
return 0;
} |
18,750 | __device__ void rkck(float* y, float* dydx, const float x,
const float h, float* yout, float* yerr,
void derivs(const float, float* , float* ))
{
const float a2=0.2, a3=0.3, a4=0.6, a5=1.0, a6=0.875,
b21=0.2, b31=3.0/40.0, b32=9.0/40.0, b41=0.3, b42 = -0.9,
b43=1.2, b51 = -11.0/54.0, b52=2.5, b53 = -70.0/27.0,
b54=35.0/27.0, b61=1631.0/55296.0, b62=175.0/512.0,
b63=575.0/13824.0, b64=44275.0/110592.0, b65=253.0/4096.0,
c1=37.0/378.0, c3=250.0/621.0, c4=125.0/594.0, c6=512.0/1771.0,
dc1=c1-2825.0/27648.0, dc3=c3-18575.0/48384.0,
dc4=c4-13525.0/55296.0, dc5 = -277.00/14336.0, dc6=c6-0.25;
int i;
const int n = 5;
float ak2[n],ak3[n],ak4[n],ak5[n],ak6[n],ytemp[n];
for (i=0;i<n;i++)
ytemp[i]=y[i]+b21*h*dydx[i];
derivs(x+a2*h,ytemp,ak2);
for (i=0;i<n;i++)
ytemp[i]=y[i]+h*(b31*dydx[i]+b32*ak2[i]);
derivs(x+a3*h,ytemp,ak3);
for (i=0;i<n;i++)
ytemp[i]=y[i]+h*(b41*dydx[i]+b42*ak2[i]+b43*ak3[i]);
derivs(x+a4*h,ytemp,ak4);
for (i=0;i<n;i++)
ytemp[i]=y[i]+h*(b51*dydx[i]+b52*ak2[i]+b53*ak3[i]+b54*ak4[i]);
derivs(x+a5*h,ytemp,ak5);
for (i=0;i<n;i++)
ytemp[i]=y[i]+h*(b61*dydx[i]+b62*ak2[i]+b63*ak3[i]+b64*ak4[i]+b65*ak5[i]);
derivs(x+a6*h,ytemp,ak6);
for (i=0;i<n;i++)
yout[i]=y[i]+h*(c1*dydx[i]+c3*ak3[i]+c4*ak4[i]+c6*ak6[i]);
for (i=0;i<n;i++)
yerr[i]=h*(dc1*dydx[i]+dc3*ak3[i]+dc4*ak4[i]+dc5*ak5[i]+dc6*ak6[i]);
}
|
18,751 |
#include <stdio.h>
__global__ void kernel(){
}
int main(void){
kernel <<<1,1>>> ();
printf("Hola, soy tu esclavo!\n");
return 0;
}
|
18,752 | /* Example of using lodepng to load, process, save image */
#include <stdio.h>
#include <stdlib.h>
#define N 4 // grid side length
#define RHO 0.5 // related to pitch
#define ETA 2e-4 // related to duration of sound
#define BOUNDARY_GAIN 0.75 // clamped edge vs free edge
#define BLOCK_WIDTH 512
#define ind(i,j) ((j) + ((i)*(N)))
//Putting blocks of size width divided by 0, so that each thread can access the neighboring values. There is no neighboring value that is called twice.
__global__ void grid_N(float * u_out, float * u1_in,float * u2_in){
int ind = blockIdx.x * blockDim.x + threadIdx.x;
int i = ((ind) / ((N))) + 1;
int j = ((ind) % (N)) + 1;
if(i<N && j < N){
if(i< N-1 && j<N-1){
//do work
float sum_of_neighbors, previous_value, previous_previous_value;
sum_of_neighbors = u1_in[ind(i-1,j)] + u1_in[ind(i+1,j)] + u1_in[ind(i,j-1)] + u1_in[ind(i,j+1)];
previous_value = u1_in[ind(i,j)];
previous_previous_value = u2_in[ind(i,j)];
u_out[ind(i,j)] = (RHO * (sum_of_neighbors -4*previous_value) + 2*previous_value -(1-ETA)*previous_previous_value)/(1+ETA);
}
__syncthreads();
if(i< N-1){
//do work
float value,to_use;
if(j == 1){
to_use = u_out[ind(1,i)];
value = BOUNDARY_GAIN * to_use; // top
u_out[ind(0,i)] = value;
to_use = u_out[ind(N-2,i)];
value = BOUNDARY_GAIN * to_use; // bottom
u_out[ind(N-1,i)] = value;
}else if(j == 2){
to_use = u_out[ind(i,1)];
value = BOUNDARY_GAIN * to_use; // left
u_out[ind(i,0)] = value;
to_use = u_out[ind(i,N-2)];
value = BOUNDARY_GAIN * to_use; // right
u_out[ind(i,N-1)] = value;
}
}
__syncthreads();
}
i = i -1;
if(i< N && j == 1){
// update corners
float value,to_use;
if(i == 0){
to_use = u_out[ind(1,i)];
value = BOUNDARY_GAIN * to_use;
u_out[ind(0,0)] = value;
}else if(i == 1){
to_use = u_out[ind(N-2,0)];
value = BOUNDARY_GAIN * to_use;
u_out[ind(N-1,0)] = value;
}else if(i == 2){
to_use = u_out[ind(0,N-2)];
value = BOUNDARY_GAIN * to_use;
u_out[ind(0,N-1)] = to_use;
}else if(i == 3){
to_use = u_out[ind(N-1,N-2)];
value = BOUNDARY_GAIN * to_use;
u_out[ind(N-1,N-1)] = value;
}
}
}
int process(int T){
// initialize grid
float *u = (float *) malloc(N * N * sizeof(float *));
float *u1 = (float *) malloc(N * N * sizeof(float *));
float *u2 = (float *) malloc(N * N * sizeof(float *));
int i,j;
for (i = 0; i < N*N; i++) {
u[i] = 0;
u1[i] = 0;
u2[i] = 0;
}
printf("Size of grid: %d nodes\n", N*N);
// simulate drum strike
u1[ind(N/2,N/2)] = 1;
float *audio = (float *) malloc(T * sizeof(float));
const int size = N * N * sizeof(float);
// declare GPU memory pointers
float * u1_in;
float * u2_in;
float * u_out;
float * temp;
// allocate GPU memory
cudaMalloc(&u1_in, size);
cudaMalloc(&u2_in, size);
cudaMalloc(&u_out, size);
int t;
for (t = 0; t < T; t++) {
// printf("Run %d | %d total size with width %d and height %d in %d blocks of size %d. Size of memory %d\n",t,(N*N),N,N, ((N*N)+(BLOCK_WIDTH-1))/BLOCK_WIDTH, BLOCK_WIDTH,size);
printf("Try printing %f %f %f\n",u[ind(N/2,N/2)],u1[ind(N/2,N/2)],u2[ind(N/2,N/2)]);
for (i = 0; i < N; i++) {
for (j = 0; j < N; j++) {
printf("u(%d,%d) %f |",i,j,u[ind(i,j)]);
}
printf("\n");
}
printf("\n");
for (i = 0; i < N; i++) {
for (j = 0; j < N; j++) {
printf("u1(%d,%d) %f |",i,j,u1[ind(i,j)]);
}
printf("\n");
}
printf("\n");
for (i = 0; i < N; i++) {
for (j = 0; j < N; j++) {
printf("u2(%d,%d) %f |",i,j,u2[ind(i,j)]);
}
printf("\n");
}
// transfer the array to the GPU
cudaMemcpy(u1_in, u1, size, cudaMemcpyHostToDevice);
cudaMemcpy(u2_in, u2, size, cudaMemcpyHostToDevice);
// launch the kernel
dim3 dimGrid(((N*N)+(BLOCK_WIDTH-1))/BLOCK_WIDTH);
dim3 dimBlock(BLOCK_WIDTH);
grid_N<<<dimGrid, dimBlock>>>(u_out,u1_in,u2_in);
// copy back the result array to the CPU
cudaMemcpy(u, u_out, size, cudaMemcpyDeviceToHost);
cudaError_t error1 = cudaGetLastError();
if (error1 != cudaSuccess)printf("kernel 1 launch failed: %s\n",cudaGetErrorString(error1));
cudaThreadSynchronize();
cudaError_t error2 = cudaGetLastError();
if (error2 != cudaSuccess)printf("kernel 1 execution failed: %s\n",cudaGetErrorString(error2));
// print_grid(u);
audio[t] = u[ind(N/2,N/2)];
printf("%f,\n", audio[t]);
temp = u2;
u2 = u1;
u1 = u;
u = temp;
}
cudaFree(u2_in);
cudaFree(u1_in);
cudaFree(u_out);
free(u);
free(u1);
free(u2);
free(audio);
return 0;
}
int main(int argc, char *argv[]){
if ( argc >= 2 ){
int T = atoi(argv[1]);
int error = process(T);
if(error != 0){
printf("An error occured. ( %d )\n",error);
}else{
printf("The rectification ran with success.\n");
}
}else{
printf("There is inputs missing.\n");
}
return 0;
}
|
18,753 | /********************************************************************/
/***** GPU Graph Cut ************************************************/
/********************************************************************/
////////////////////////////////////////////////////
// Copyright (c) 2018 Kiyoshi Oguri 2018.02.14 //
// Released under the MIT license //
// http://opensource.org/licenses/mit-license.php //
////////////////////////////////////////////////////
extern int cost(int S, int H, int W);
extern void cut(int S, int H, int W);
#define TD_NUM (256)
#define LOOP (37)
int SIZE_S;
int SIZE_H;
int SIZE_W;
int SIZE_HW;
int SIZE_SHW;
__constant__ int size_s;
__constant__ int size_h;
__constant__ int size_w;
__constant__ int size_hw;
__constant__ int size_shw;
int *h_FLG;
int *h_FLW;
int *h_OVF;
int *h_HGT;
int *d_FLG;
int *d_FLW;
int *d_OVF;
int *d_HGT;
int *d_TAG;
__constant__ int *FLW;
__constant__ int *OVF;
__constant__ int *HGT;
__constant__ int *TAG;
size_t I_SIZE;
size_t FLW_SIZE;
size_t OVF_SIZE;
size_t HGT_SIZE;
size_t TAG_SIZE;
#define h_ADR1(S, H, W) \
((S)*SIZE_HW+\
(H)*SIZE_W+\
(W))
#define h_ADR2(S, H, W, D) \
((D)*SIZE_SHW+\
(S)*SIZE_HW+\
(H)*SIZE_W+\
(W))
#define ADR1(S, H, W) \
((S)*size_hw+\
(H)*size_w+\
(W))
#define ADR2(S, H, W, D) \
((D)*size_shw+\
(S)*size_hw+\
(H)*size_w+\
(W))
#define SADR1(S, H, W) \
((S)*SIZE_BHW+\
(H)*SIZE_BW+\
(W))
#define DIV(A,B) (((A)%(B)==0)? ((A)/(B)) : (((A)/(B))+1))
#define SIZE_BS (8)
#define SIZE_BH (8)
#define SIZE_BW (8)
#define SIZE_BSHW (SIZE_BS*SIZE_BH*SIZE_BW)
#define SIZE_BHW (SIZE_BH*SIZE_BW)
#define SIZE_GS DIV(SIZE_S,SIZE_BS)
#define SIZE_GH DIV(SIZE_H,SIZE_BH)
#define SIZE_GW DIV(SIZE_W,SIZE_BW)
#define Out_Mask 0x000003ff
#define Out0_Set 0x00000001
#define Out1_Set 0x00000002
#define Out2_Set 0x00000004
#define Out3_Set 0x00000008
#define Out4_Set 0x00000010
#define Out5_Set 0x00000020
#define Out6_Set 0x00000040
#define Out7_Set 0x00000080
#define Out8_Set 0x00000100
#define Out9_Set 0x00000200
#define Out0_Rst ~Out0_Set
#define Out1_Rst ~Out1_Set
#define Out2_Rst ~Out2_Set
#define Out3_Rst ~Out3_Set
#define Out4_Rst ~Out4_Set
#define Out5_Rst ~Out5_Set
#define Out6_Rst ~Out6_Set
#define Out7_Rst ~Out7_Set
#define Out8_Rst ~Out8_Set
#define Out9_Rst ~Out9_Set
inline __device__ int edg_read(int S, int H, int W, int D) {
return FLW[ADR2(S, H, W, D)];
}
inline __device__ void edg_add(int S, int H, int W, int D, int V) {
FLW[ADR2(S, H, W, D)] += V;
}
inline __device__ int ovf_read(int S, int H, int W) {
return OVF[ADR1(S, H, W)];
}
inline __device__ void ovf_add(int S, int H, int W, int V) {
atomicAdd(&(OVF[ADR1(S, H, W)]), V);
}
inline __device__ int hgt_read(int S, int H, int W) {
return HGT[ADR1(S, H, W)];
}
inline __device__ void hgt_write(int S, int H, int W, int V) {
HGT[ADR1(S, H, W)] = V;
}
inline __device__ int tag_read(int S, int H, int W) {
return TAG[ADR1(S, H, W)];
}
inline __device__ void tag_set(int S, int H, int W, int M) {
atomicOr(&(TAG[ADR1(S, H, W)]), M);
}
inline __device__ void tag_rst(int S, int H, int W, int M) {
atomicAnd(&(TAG[ADR1(S, H, W)]), M);
}
inline __device__ int shed_read(int SM[], int S, int H, int W) {
return SM[SADR1(S, H, W)];
}
inline __device__ void shed_write(int SM[], int S, int H, int W, int V) {
SM[SADR1(S, H, W)] = V;
}
__global__ void reset(int FLG[]) {
FLG[0] = 0;
}
__device__ void out_chk(int S, int H, int W, int D, int SM) {
if (edg_read(S, H, W, D) > 0) tag_set(S, H, W, SM);
}
__global__ void tag_init(void) {
///////////////////////////////
int total_id = blockDim.x * blockIdx.x + threadIdx.x;
if (total_id >= size_shw) return;
int S = total_id / size_hw;
int sa = total_id % size_hw;
int H = sa / size_w;
int W = sa % size_w;
///////////////////////////////
tag_rst(S, H, W, ~Out_Mask);
out_chk(S, H, W, 0, Out0_Set);
out_chk(S, H, W, 1, Out1_Set);
out_chk(S, H, W, 2, Out2_Set);
out_chk(S, H, W, 3, Out3_Set);
out_chk(S, H, W, 4, Out4_Set);
out_chk(S, H, W, 5, Out5_Set);
out_chk(S, H, W, 6, Out6_Set);
out_chk(S, H, W, 7, Out7_Set);
out_chk(S, H, W, 8, Out8_Set);
out_chk(S, H, W, 9, Out9_Set);
}
__global__ void bfs_init(void) {
///////////////////////////////
int total_id = blockDim.x * blockIdx.x + threadIdx.x;
if (total_id >= size_shw) return;
int S = total_id / size_hw;
int sa = total_id % size_hw;
int H = sa / size_w;
int W = sa % size_w;
///////////////////////////////
if (ovf_read(S, H, W) < 0) hgt_write(S, H, W, 0);
else hgt_write(S, H, W, size_shw);
}
__device__ void bfs_front_i(int SM[], int S, int H, int W, int s, int h, int w, int &nt) {
///////////////////////////////
int nS = S + s;
int nH = H + h;
int nW = W + w;
///////////////////////////////
int T = shed_read(SM, nS, nH, nW) + 1;
if (nt > T) nt = T;
}
__global__ void bfs_do_i(void) {
__shared__ int FLG;
__shared__ int SM[SIZE_BSHW];
///////////////////////////////
int bs = blockIdx.z;
int bh = blockIdx.y;
int bw = blockIdx.x;
//---------------------------//
int s = threadIdx.z;
int h = threadIdx.y;
int w = threadIdx.x;
//---------------------------//
int S = SIZE_BS * bs + s;
if (S >= size_s) return;
int H = SIZE_BH * bh + h;
if (H >= size_h) return;
int W = SIZE_BW * bw + w;
if (W >= size_w) return;
///////////////////////////////
bool nzz = (s != 0);
bool nzy = (h != 0);
bool nzx = (w != 0);
bool niz = ((s != (SIZE_BS-1)) && (S != (size_s-1)));
bool niy = ((h != (SIZE_BH-1)) && (H != (size_h-1)));
bool nix = ((w != (SIZE_BW-1)) && (W != (size_w-1)));
///////////////////////////////
int tag = tag_read(S, H, W);
shed_write(SM, s, h, w, hgt_read(S, H, W));
for ( ; ; ) {
__syncthreads();
if ((s == 0) && (h == 0) && (w == 0)) FLG = 0;
__syncthreads();
if (tag & Out_Mask) {
int ct = shed_read(SM, s, h, w);
int nt = ct;
if (niz ) if (tag & Out0_Set) bfs_front_i(SM, s, h, w, 1, 0, 0, nt);
if (niz && nix) if (tag & Out5_Set) bfs_front_i(SM, s, h, w, 1, 0, 1, nt);
if (niz && nzx) if (tag & Out6_Set) bfs_front_i(SM, s, h, w, 1, 0,-1, nt);
if ( niy) if (tag & Out2_Set) bfs_front_i(SM, s, h, w, 0, 1, 0, nt);
if ( nzy) if (tag & Out1_Set) bfs_front_i(SM, s, h, w, 0,-1, 0, nt);
if ( nix) if (tag & Out4_Set) bfs_front_i(SM, s, h, w, 0, 0, 1, nt);
if ( nzx) if (tag & Out3_Set) bfs_front_i(SM, s, h, w, 0, 0,-1, nt);
if (nzz && nix) if (tag & Out8_Set) bfs_front_i(SM, s, h, w,-1, 0, 1, nt);
if (nzz && nzx) if (tag & Out7_Set) bfs_front_i(SM, s, h, w,-1, 0,-1, nt);
if (nzz ) if (tag & Out9_Set) bfs_front_i(SM, s, h, w,-1, 0, 0, nt);
if (nt != ct) {
shed_write(SM, s, h, w, nt);
if (FLG == 0) FLG = 1;
}
}
__syncthreads();
if (FLG == 0) break;
}
hgt_write(S, H, W, shed_read(SM, s, h, w));
}
__device__ void bfs_front_o(int S, int H, int W, int s, int h, int w, int &nt) {
///////////////////////////////
int nS = S + s;
int nH = H + h;
int nW = W + w;
///////////////////////////////
int T = hgt_read(nS, nH, nW) + 1;
if (nt > T) nt = T;
}
__global__ void bfs_do_o(int FLG[]) {
///////////////////////////////
int bs = blockIdx.z;
int bh = blockIdx.y;
int bw = blockIdx.x;
//---------------------------//
int s = threadIdx.z;
int h = threadIdx.y;
int w = threadIdx.x;
//---------------------------//
int S = SIZE_BS * bs + s;
if (S >= size_s) return;
int H = SIZE_BH * bh + h;
if (H >= size_h) return;
int W = SIZE_BW * bw + w;
if (W >= size_w) return;
///////////////////////////////
bool pzz = ((s == 0) && (S != 0));
bool pzy = ((h == 0) && (H != 0));
bool pzx = ((w == 0) && (W != 0));
bool piz = ((s == (SIZE_BS-1)) && (S != (size_s-1)));
bool piy = ((h == (SIZE_BH-1)) && (H != (size_h-1)));
bool pix = ((w == (SIZE_BW-1)) && (W != (size_w-1)));
///////////////////////////////
int tag = tag_read(S, H, W);
if (tag & Out_Mask) {
int ct = hgt_read(S, H, W);
int nt = ct;
if (piz ) if (tag & Out0_Set) bfs_front_o(S, H, W, 1, 0, 0, nt);
if (piz || pix) if (tag & Out5_Set) bfs_front_o(S, H, W, 1, 0, 1, nt);
if (piz || pzx) if (tag & Out6_Set) bfs_front_o(S, H, W, 1, 0,-1, nt);
if ( piy) if (tag & Out2_Set) bfs_front_o(S, H, W, 0, 1, 0, nt);
if ( pzy) if (tag & Out1_Set) bfs_front_o(S, H, W, 0,-1, 0, nt);
if ( pix) if (tag & Out4_Set) bfs_front_o(S, H, W, 0, 0, 1, nt);
if ( pzx) if (tag & Out3_Set) bfs_front_o(S, H, W, 0, 0,-1, nt);
if (pzz || pix) if (tag & Out8_Set) bfs_front_o(S, H, W,-1, 0, 1, nt);
if (pzz || pzx) if (tag & Out7_Set) bfs_front_o(S, H, W,-1, 0,-1, nt);
if (pzz ) if (tag & Out9_Set) bfs_front_o(S, H, W,-1, 0, 0, nt);
if (nt != ct) {
hgt_write(S, H, W, nt);
if (FLG[0] == 0) FLG[0] = 1;
}
}
}
__global__ void ovf_do(int FLG[]) {
if (FLG[0] != 0) return;
///////////////////////////////
int total_id = blockDim.x * blockIdx.x + threadIdx.x;
if (total_id >= size_shw) return;
int S = total_id / size_hw;
int sa = total_id % size_hw;
int H = sa / size_w;
int W = sa % size_w;
///////////////////////////////
if (hgt_read(S, H, W) == size_shw) return;
if (ovf_read(S, H, W) <= 0) return;
FLG[0] = 1;
}
__device__ void push1(int S, int H, int W, int s, int h, int w, int D, int R, int SM, int RM, int hh, int &oo) {
///////////////////////////////
int nS = S + s;
int nH = H + h;
int nW = W + w;
///////////////////////////////
if (hgt_read(nS, nH, nW) >= hh) return;
int mm = edg_read(S, H, W, D);
bool qq = oo >= mm;
int pp = qq? mm: oo;
ovf_add(nS, nH, nW, pp);
ovf_add(S, H, W, -pp);
edg_add(nS, nH, nW, R, pp);
edg_add(S, H, W, D, -pp);
oo -= pp;
if (qq) tag_rst(S, H, W, RM);
tag_set(nS, nH, nW, SM);
}
__global__ void push(void) {
///////////////////////////////
int total_id = blockDim.x * blockIdx.x + threadIdx.x;
if (total_id >= size_shw) return;
int S = total_id / size_hw;
int sa = total_id % size_hw;
int H = sa / size_w;
int W = sa % size_w;
///////////////////////////////
int hh = hgt_read(S, H, W);
if (hh == size_shw) return;
int oo = ovf_read(S, H, W);
if (oo <= 0) return;
int tag = tag_read(S, H, W);
if ((tag & Out_Mask) == 0) return;
if (tag & Out0_Set) push1(S, H, W, 1, 0, 0, 0, 9, Out9_Set, Out0_Rst, hh, oo); if (oo <= 0) return;
if (tag & Out5_Set) push1(S, H, W, 1, 0, 1, 5, 7, Out7_Set, Out5_Rst, hh, oo); if (oo <= 0) return;
if (tag & Out6_Set) push1(S, H, W, 1, 0,-1, 6, 8, Out8_Set, Out6_Rst, hh, oo); if (oo <= 0) return;
if (tag & Out2_Set) push1(S, H, W, 0, 1, 0, 2, 1, Out1_Set, Out2_Rst, hh, oo); if (oo <= 0) return;
if (tag & Out1_Set) push1(S, H, W, 0,-1, 0, 1, 2, Out2_Set, Out1_Rst, hh, oo); if (oo <= 0) return;
if (tag & Out4_Set) push1(S, H, W, 0, 0, 1, 4, 3, Out3_Set, Out4_Rst, hh, oo); if (oo <= 0) return;
if (tag & Out3_Set) push1(S, H, W, 0, 0,-1, 3, 4, Out4_Set, Out3_Rst, hh, oo); if (oo <= 0) return;
if (tag & Out8_Set) push1(S, H, W,-1, 0, 1, 8, 6, Out6_Set, Out8_Rst, hh, oo); if (oo <= 0) return;
if (tag & Out7_Set) push1(S, H, W,-1, 0,-1, 7, 5, Out5_Set, Out7_Rst, hh, oo); if (oo <= 0) return;
if (tag & Out9_Set) push1(S, H, W,-1, 0, 0, 9, 0, Out0_Set, Out9_Rst, hh, oo); if (oo <= 0) return;
hgt_write(S, H, W, hh + 1);
}
void push_relabel(int loop) {
dim3 grid(SIZE_GW, SIZE_GH, SIZE_GS);
dim3 block(SIZE_BW, SIZE_BH, SIZE_BS);
tag_init<<< (SIZE_SHW/TD_NUM)+1, TD_NUM >>>();
for ( ; ; ) {
bfs_init<<< (SIZE_SHW/TD_NUM)+1, TD_NUM >>>();
for ( ; ; ) {
bfs_do_i<<< grid, block >>>();
reset<<< 1, 1 >>>(d_FLG);
bfs_do_o<<< grid, block >>>(d_FLG);
cudaThreadSynchronize();
cudaMemcpy(h_FLG, d_FLG, I_SIZE, cudaMemcpyDeviceToHost);
if (h_FLG[0] == 0) break;
}
reset<<< 1, 1 >>>(d_FLG);
ovf_do<<< (SIZE_SHW/TD_NUM)+1, TD_NUM >>>(d_FLG);
cudaThreadSynchronize();
cudaMemcpy(h_FLG, d_FLG, I_SIZE, cudaMemcpyDeviceToHost);
if (h_FLG[0] == 0) break;
for (int i = 0; i < loop; i++) {
push<<< (SIZE_SHW/TD_NUM)+1, TD_NUM >>>();
}
}
}
void data_set(int penalty_w, int penalty_h, int inhibit_a, int inhibit_b) {
for (int H = 0; H < SIZE_H; H++) {
for (int W = 0; W < SIZE_W; W++) {
for (int S = 0; S < SIZE_S; S++) {
///////////////////////////////
for (int i = 0; i < 10; i++) h_FLW[h_ADR2(S, H, W, i)] = 0;
h_OVF[h_ADR1(S, H, W)] = 0;
///////////////////////////////
if (S!=SIZE_S-1) h_FLW[h_ADR2(S, H, W, 0)] = cost(S+1, H, W);
if (S==SIZE_S-1) h_OVF[h_ADR1(S, H, W)] -= cost(S+1, H, W);
if (S==0) h_OVF[h_ADR1(S, H, W)] += cost(S, H, W);
if (S!=0) h_FLW[h_ADR2(S, H, W, 9)] = inhibit_a;
if (H!=0) h_FLW[h_ADR2(S, H, W, 1)] = penalty_h;
if (H!=SIZE_H-1) h_FLW[h_ADR2(S, H, W, 2)] = penalty_h;
if (W!=0) h_FLW[h_ADR2(S, H, W, 3)] = penalty_w;
if (W!=SIZE_W-1) h_FLW[h_ADR2(S, H, W, 4)] = penalty_w;
if ((S!=0)&&(W!=0)) h_FLW[h_ADR2(S, H, W, 7)] = inhibit_b;
if ((S!=0)&&(W!=SIZE_W-1)) h_FLW[h_ADR2(S, H, W, 8)] = inhibit_b;
}
}
}
cudaMemcpy(d_FLW, h_FLW, FLW_SIZE, cudaMemcpyHostToDevice);
cudaMemcpy(d_OVF, h_OVF, OVF_SIZE, cudaMemcpyHostToDevice);
}
int sink_chk(void) {
cudaMemcpy(h_OVF, d_OVF, OVF_SIZE, cudaMemcpyDeviceToHost);
int total = 0;
for (int H = 0; H < SIZE_H; H++) {
for (int W = 0; W < SIZE_W; W++) {
int sink = h_OVF[h_ADR1(SIZE_S-1, H, W)];
if (sink < 0) total += -sink;
}
}
return total;
}
void dep_set(void) {
cudaMemcpy(h_HGT, d_HGT, HGT_SIZE, cudaMemcpyDeviceToHost);
for (int H = 0; H < SIZE_H; H++) {
for (int W = 0; W < SIZE_W; W++) {
for (int S = SIZE_S; S >= 0; S--) {
if (S == SIZE_S) {
if (h_HGT[h_ADR1(S-1, H, W)] == SIZE_SHW) {
cut(S, H, W);
break;
}
}
else if (S == 0) {
if (h_HGT[h_ADR1(S, H, W)] != SIZE_SHW) {
cut(S, H, W);
break;
}
}
else {
if ((h_HGT[h_ADR1(S, H, W)] != SIZE_SHW) && (h_HGT[h_ADR1(S-1, H, W)] == SIZE_SHW)) {
cut(S, H, W);
break;
}
}
}
}
}
}
int graph_cut(int penalty_w, int penalty_h, int inhibit_a, int inhibit_b) {
cudaMemcpyToSymbol(size_s, &SIZE_S, sizeof(int));
cudaMemcpyToSymbol(size_h, &SIZE_H, sizeof(int));
cudaMemcpyToSymbol(size_w, &SIZE_W, sizeof(int));
cudaMemcpyToSymbol(size_hw, &SIZE_HW, sizeof(int));
cudaMemcpyToSymbol(size_shw, &SIZE_SHW, sizeof(int));
I_SIZE = sizeof(int);
FLW_SIZE = sizeof(int) * SIZE_SHW*10;
OVF_SIZE = sizeof(int) * SIZE_SHW;
HGT_SIZE = sizeof(int) * SIZE_SHW;
TAG_SIZE = sizeof(int) * SIZE_SHW;
h_FLG = new int[1];
h_FLW = new int[SIZE_SHW*10];
h_OVF = new int[SIZE_SHW];
h_HGT = new int[SIZE_SHW];
cudaMalloc((void **)&d_FLG, I_SIZE);
cudaMalloc((void **)&d_FLW, FLW_SIZE);
cudaMalloc((void **)&d_OVF, OVF_SIZE);
cudaMalloc((void **)&d_HGT, HGT_SIZE);
cudaMalloc((void **)&d_TAG, TAG_SIZE);
cudaMemcpyToSymbol(FLW, &d_FLW, sizeof(int*));
cudaMemcpyToSymbol(OVF, &d_OVF, sizeof(int*));
cudaMemcpyToSymbol(HGT, &d_HGT, sizeof(int*));
cudaMemcpyToSymbol(TAG, &d_TAG, sizeof(int*));
data_set(penalty_w, penalty_h, inhibit_a, inhibit_b);
int before = sink_chk();
push_relabel(LOOP);
int after = sink_chk();
dep_set();
cudaFree(d_TAG);
cudaFree(d_HGT);
cudaFree(d_OVF);
cudaFree(d_FLW);
cudaFree(d_FLG);
delete [] h_HGT;
delete [] h_OVF;
delete [] h_FLW;
delete [] h_FLG;
return before - after;
}
|
18,754 | #include "includes.h"
__global__ void update_old( float4 *__restrict__ newPos, float4 *__restrict__ oldPos )
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
oldPos[index] = newPos[index];
} |
18,755 | #include "includes.h"
__device__ bool checkBoundary(int blockIdx, int blockDim, int threadIdx){
int x = threadIdx;
int y = blockIdx;
return (x == 0 || x == (blockDim-1) || y == 0 || y == 479);
}
__global__ void mJocobi_TwoDim(float *x_new, float *x_old, float* b, float alpha, float rBeta) {
if(checkBoundary(blockIdx.x, blockDim.x, threadIdx.x)) return;
int Idx = blockIdx.x * blockDim.x + threadIdx.x;
int Left = Idx - 1;
int Right = Idx + 1;
int Top = Idx + blockDim.x;
int Bottom = Idx - blockDim.x;
x_new[Idx] = ((x_old[Left]+x_old[Right]+x_old[Top]+x_old[Bottom])*alpha + b[Idx])*rBeta;
} |
18,756 | //=====================================================================
// MAIN FUNCTION
//=====================================================================
__device__ void kernel_ecc(float timeinst, float* d_initvalu, float *d_finavalu, int valu_offset,
float* d_params) {
//=====================================================================
// VARIABLES
//=====================================================================
// input parameters
float cycleLength;
// variable references // GET VARIABLES FROM MEMORY AND SAVE LOCALLY !!!!!!!!!!!!!!!!!!
int offset_1;
int offset_2;
int offset_3;
int offset_4;
int offset_5;
int offset_6;
int offset_7;
int offset_8;
int offset_9;
int offset_10;
int offset_11;
int offset_12;
int offset_13;
int offset_14;
int offset_15;
int offset_16;
int offset_17;
int offset_18;
int offset_19;
int offset_20;
int offset_21;
int offset_22;
int offset_23;
int offset_24;
int offset_25;
int offset_26;
int offset_27;
int offset_28;
int offset_29;
int offset_30;
int offset_31;
int offset_32;
int offset_33;
int offset_34;
int offset_35;
int offset_36;
int offset_37;
int offset_38;
int offset_39;
int offset_40;
int offset_41;
int offset_42;
int offset_43;
int offset_44;
int offset_45;
int offset_46;
// stored input array
float d_initvalu_1;
float d_initvalu_2;
float d_initvalu_3;
float d_initvalu_4;
float d_initvalu_5;
float d_initvalu_6;
float d_initvalu_7;
float d_initvalu_8;
float d_initvalu_9;
float d_initvalu_10;
float d_initvalu_11;
float d_initvalu_12;
float d_initvalu_13;
float d_initvalu_14;
float d_initvalu_15;
float d_initvalu_16;
float d_initvalu_17;
float d_initvalu_18;
float d_initvalu_19;
float d_initvalu_20;
float d_initvalu_21;
// float d_initvalu_22;
float d_initvalu_23;
float d_initvalu_24;
float d_initvalu_25;
float d_initvalu_26;
float d_initvalu_27;
float d_initvalu_28;
float d_initvalu_29;
float d_initvalu_30;
float d_initvalu_31;
float d_initvalu_32;
float d_initvalu_33;
float d_initvalu_34;
float d_initvalu_35;
float d_initvalu_36;
float d_initvalu_37;
float d_initvalu_38;
float d_initvalu_39;
float d_initvalu_40;
// float d_initvalu_41;
// float d_initvalu_42;
// float d_initvalu_43;
// float d_initvalu_44;
// float d_initvalu_45;
// float d_initvalu_46;
// matlab constants undefined in c
float pi;
// Constants
float R; // [J/kmol*K]
float Frdy; // [C/mol]
float Temp; // [K] 310
float FoRT; //
float Cmem; // [F] membrane capacitance
float Qpow;
// Cell geometry
float cellLength; // cell length [um]
float cellRadius; // cell radius [um]
// float junctionLength; // junc length [um]
// float junctionRadius; // junc radius [um]
// float distSLcyto; // dist. SL to cytosol [um]
// float distJuncSL; // dist. junc to SL [um]
// float DcaJuncSL; // Dca junc to SL [cm^2/sec]
// float DcaSLcyto; // Dca SL to cyto [cm^2/sec]
// float DnaJuncSL; // Dna junc to SL [cm^2/sec]
// float DnaSLcyto; // Dna SL to cyto [cm^2/sec]
float Vcell; // [L]
float Vmyo;
float Vsr;
float Vsl;
float Vjunc;
// float SAjunc; // [um^2]
// float SAsl; // [um^2]
float J_ca_juncsl; // [L/msec]
float J_ca_slmyo; // [L/msec]
float J_na_juncsl; // [L/msec]
float J_na_slmyo; // [L/msec]
// Fractional currents in compartments
float Fjunc;
float Fsl;
float Fjunc_CaL;
float Fsl_CaL;
// Fixed ion concentrations
float Cli; // Intracellular Cl [mM]
float Clo; // Extracellular Cl [mM]
float Ko; // Extracellular K [mM]
float Nao; // Extracellular Na [mM]
float Cao; // Extracellular Ca [mM]
float Mgi; // Intracellular Mg [mM]
// Nernst Potentials
float ena_junc; // [mV]
float ena_sl; // [mV]
float ek; // [mV]
float eca_junc; // [mV]
float eca_sl; // [mV]
float ecl; // [mV]
// Na transport parameters
float GNa; // [mS/uF]
float GNaB; // [mS/uF]
float IbarNaK; // [uA/uF]
float KmNaip; // [mM]
float KmKo; // [mM]
// float Q10NaK;
// float Q10KmNai;
// K current parameters
float pNaK;
float GtoSlow; // [mS/uF]
float GtoFast; // [mS/uF]
float gkp;
// Cl current parameters
float GClCa; // [mS/uF]
float GClB; // [mS/uF]
float KdClCa; // [mM] // [mM]
// I_Ca parameters
float pNa; // [cm/sec]
float pCa; // [cm/sec]
float pK; // [cm/sec]
// float KmCa; // [mM]
float Q10CaL;
// Ca transport parameters
float IbarNCX; // [uA/uF]
float KmCai; // [mM]
float KmCao; // [mM]
float KmNai; // [mM]
float KmNao; // [mM]
float ksat; // [none]
float nu; // [none]
float Kdact; // [mM]
float Q10NCX; // [none]
float IbarSLCaP; // [uA/uF]
float KmPCa; // [mM]
float GCaB; // [uA/uF]
float Q10SLCaP; // [none] // [none]
// SR flux parameters
float Q10SRCaP; // [none]
float Vmax_SRCaP; // [mM/msec] (mmol/L cytosol/msec)
float Kmf; // [mM]
float Kmr; // [mM]L cytosol
float hillSRCaP; // [mM]
float ks; // [1/ms]
float koCa; // [mM^-2 1/ms]
float kom; // [1/ms]
float kiCa; // [1/mM/ms]
float kim; // [1/ms]
float ec50SR; // [mM]
// Buffering parameters
float Bmax_Naj; // [mM]
float Bmax_Nasl; // [mM]
float koff_na; // [1/ms]
float kon_na; // [1/mM/ms]
float Bmax_TnClow; // [mM], TnC low affinity
float koff_tncl; // [1/ms]
float kon_tncl; // [1/mM/ms]
float Bmax_TnChigh; // [mM], TnC high affinity
float koff_tnchca; // [1/ms]
float kon_tnchca; // [1/mM/ms]
float koff_tnchmg; // [1/ms]
float kon_tnchmg; // [1/mM/ms]
// float Bmax_CaM; // [mM], CaM buffering
// float koff_cam; // [1/ms]
// float kon_cam; // [1/mM/ms]
float Bmax_myosin; // [mM], Myosin buffering
float koff_myoca; // [1/ms]
float kon_myoca; // [1/mM/ms]
float koff_myomg; // [1/ms]
float kon_myomg; // [1/mM/ms]
float Bmax_SR; // [mM]
float koff_sr; // [1/ms]
float kon_sr; // [1/mM/ms]
float Bmax_SLlowsl; // [mM], SL buffering
float Bmax_SLlowj; // [mM]
float koff_sll; // [1/ms]
float kon_sll; // [1/mM/ms]
float Bmax_SLhighsl; // [mM]
float Bmax_SLhighj; // [mM]
float koff_slh; // [1/ms]
float kon_slh; // [1/mM/ms]
float Bmax_Csqn; // 140e-3*Vmyo/Vsr; [mM]
float koff_csqn; // [1/ms]
float kon_csqn; // [1/mM/ms]
// I_Na: Fast Na Current
float am;
float bm;
float ah;
float bh;
float aj;
float bj;
float I_Na_junc;
float I_Na_sl;
// float I_Na;
// I_nabk: Na Background Current
float I_nabk_junc;
float I_nabk_sl;
// float I_nabk;
// I_nak: Na/K Pump Current
float sigma;
float fnak;
float I_nak_junc;
float I_nak_sl;
float I_nak;
// I_kr: Rapidly Activating K Current
float gkr;
float xrss;
float tauxr;
float rkr;
float I_kr;
// I_ks: Slowly Activating K Current
float pcaks_junc;
float pcaks_sl;
float gks_junc;
float gks_sl;
float eks;
float xsss;
float tauxs;
float I_ks_junc;
float I_ks_sl;
float I_ks;
// I_kp: Plateau K current
float kp_kp;
float I_kp_junc;
float I_kp_sl;
float I_kp;
// I_to: Transient Outward K Current (slow and fast components)
float xtoss;
float ytoss;
float rtoss;
float tauxtos;
float tauytos;
float taurtos;
float I_tos;
//
float tauxtof;
float tauytof;
float I_tof;
float I_to;
// I_ki: Time-Independent K Current
float aki;
float bki;
float kiss;
float I_ki;
// I_ClCa: Ca-activated Cl Current, I_Clbk: background Cl Current
float I_ClCa_junc;
float I_ClCa_sl;
float I_ClCa;
float I_Clbk;
// I_Ca: L-type Calcium Current
float dss;
float taud;
float fss;
float tauf;
//
float ibarca_j;
float ibarca_sl;
float ibark;
float ibarna_j;
float ibarna_sl;
float I_Ca_junc;
float I_Ca_sl;
float I_Ca;
float I_CaK;
float I_CaNa_junc;
float I_CaNa_sl;
// float I_CaNa;
// float I_Catot;
// I_ncx: Na/Ca Exchanger flux
float Ka_junc;
float Ka_sl;
float s1_junc;
float s1_sl;
float s2_junc;
float s3_junc;
float s2_sl;
float s3_sl;
float I_ncx_junc;
float I_ncx_sl;
float I_ncx;
// I_pca: Sarcolemmal Ca Pump Current
float I_pca_junc;
float I_pca_sl;
float I_pca;
// I_cabk: Ca Background Current
float I_cabk_junc;
float I_cabk_sl;
float I_cabk;
// SR fluxes: Calcium Release, SR Ca pump, SR Ca leak
float MaxSR;
float MinSR;
float kCaSR;
float koSRCa;
float kiSRCa;
float RI;
float J_SRCarel; // [mM/ms]
float J_serca;
float J_SRleak; // [mM/ms]
// Cytosolic Ca Buffers
float J_CaB_cytosol;
// Junctional and SL Ca Buffers
float J_CaB_junction;
float J_CaB_sl;
// SR Ca Concentrations
float oneovervsr;
// Sodium Concentrations
float I_Na_tot_junc; // [uA/uF]
float I_Na_tot_sl; // [uA/uF]
float oneovervsl;
// Potassium Concentration
float I_K_tot;
// Calcium Concentrations
float I_Ca_tot_junc; // [uA/uF]
float I_Ca_tot_sl; // [uA/uF]
// float junc_sl;
// float sl_junc;
// float sl_myo;
// float myo_sl;
// Simulation type
int state; // 0-none; 1-pace; 2-vclamp
float I_app;
float V_hold;
float V_test;
float V_clamp;
float R_clamp;
// Membrane Potential
float I_Na_tot; // [uA/uF]
float I_Cl_tot; // [uA/uF]
float I_Ca_tot;
float I_tot;
//=====================================================================
// EXECUTION
//=====================================================================
// input parameters
cycleLength = d_params[15];
// variable references
offset_1 = valu_offset;
offset_2 = valu_offset + 1;
offset_3 = valu_offset + 2;
offset_4 = valu_offset + 3;
offset_5 = valu_offset + 4;
offset_6 = valu_offset + 5;
offset_7 = valu_offset + 6;
offset_8 = valu_offset + 7;
offset_9 = valu_offset + 8;
offset_10 = valu_offset + 9;
offset_11 = valu_offset + 10;
offset_12 = valu_offset + 11;
offset_13 = valu_offset + 12;
offset_14 = valu_offset + 13;
offset_15 = valu_offset + 14;
offset_16 = valu_offset + 15;
offset_17 = valu_offset + 16;
offset_18 = valu_offset + 17;
offset_19 = valu_offset + 18;
offset_20 = valu_offset + 19;
offset_21 = valu_offset + 20;
offset_22 = valu_offset + 21;
offset_23 = valu_offset + 22;
offset_24 = valu_offset + 23;
offset_25 = valu_offset + 24;
offset_26 = valu_offset + 25;
offset_27 = valu_offset + 26;
offset_28 = valu_offset + 27;
offset_29 = valu_offset + 28;
offset_30 = valu_offset + 29;
offset_31 = valu_offset + 30;
offset_32 = valu_offset + 31;
offset_33 = valu_offset + 32;
offset_34 = valu_offset + 33;
offset_35 = valu_offset + 34;
offset_36 = valu_offset + 35;
offset_37 = valu_offset + 36;
offset_38 = valu_offset + 37;
offset_39 = valu_offset + 38;
offset_40 = valu_offset + 39;
offset_41 = valu_offset + 40;
offset_42 = valu_offset + 41;
offset_43 = valu_offset + 42;
offset_44 = valu_offset + 43;
offset_45 = valu_offset + 44;
offset_46 = valu_offset + 45;
// stored input array
d_initvalu_1 = d_initvalu[offset_1];
d_initvalu_2 = d_initvalu[offset_2];
d_initvalu_3 = d_initvalu[offset_3];
d_initvalu_4 = d_initvalu[offset_4];
d_initvalu_5 = d_initvalu[offset_5];
d_initvalu_6 = d_initvalu[offset_6];
d_initvalu_7 = d_initvalu[offset_7];
d_initvalu_8 = d_initvalu[offset_8];
d_initvalu_9 = d_initvalu[offset_9];
d_initvalu_10 = d_initvalu[offset_10];
d_initvalu_11 = d_initvalu[offset_11];
d_initvalu_12 = d_initvalu[offset_12];
d_initvalu_13 = d_initvalu[offset_13];
d_initvalu_14 = d_initvalu[offset_14];
d_initvalu_15 = d_initvalu[offset_15];
d_initvalu_16 = d_initvalu[offset_16];
d_initvalu_17 = d_initvalu[offset_17];
d_initvalu_18 = d_initvalu[offset_18];
d_initvalu_19 = d_initvalu[offset_19];
d_initvalu_20 = d_initvalu[offset_20];
d_initvalu_21 = d_initvalu[offset_21];
// d_initvalu_22 = d_initvalu[offset_22];
d_initvalu_23 = d_initvalu[offset_23];
d_initvalu_24 = d_initvalu[offset_24];
d_initvalu_25 = d_initvalu[offset_25];
d_initvalu_26 = d_initvalu[offset_26];
d_initvalu_27 = d_initvalu[offset_27];
d_initvalu_28 = d_initvalu[offset_28];
d_initvalu_29 = d_initvalu[offset_29];
d_initvalu_30 = d_initvalu[offset_30];
d_initvalu_31 = d_initvalu[offset_31];
d_initvalu_32 = d_initvalu[offset_32];
d_initvalu_33 = d_initvalu[offset_33];
d_initvalu_34 = d_initvalu[offset_34];
d_initvalu_35 = d_initvalu[offset_35];
d_initvalu_36 = d_initvalu[offset_36];
d_initvalu_37 = d_initvalu[offset_37];
d_initvalu_38 = d_initvalu[offset_38];
d_initvalu_39 = d_initvalu[offset_39];
d_initvalu_40 = d_initvalu[offset_40];
// d_initvalu_41 = d_initvalu[offset_41];
// d_initvalu_42 = d_initvalu[offset_42];
// d_initvalu_43 = d_initvalu[offset_43];
// d_initvalu_44 = d_initvalu[offset_44];
// d_initvalu_45 = d_initvalu[offset_45];
// d_initvalu_46 = d_initvalu[offset_46];
// matlab constants undefined in c
pi = 3.1416;
// Constants
R = 8314; // [J/kmol*K]
Frdy = 96485; // [C/mol]
Temp = 310; // [K] 310
FoRT = Frdy / R / Temp; //
Cmem = 1.3810e-10; // [F] membrane capacitance
Qpow = (Temp - 310) / 10;
// Cell geometry
cellLength = 100; // cell length [um]
cellRadius = 10.25; // cell radius [um]
// junctionLength = 160e-3; // junc length [um]
// junctionRadius = 15e-3; // junc radius [um]
// distSLcyto = 0.45; // dist. SL to cytosol [um]
// distJuncSL = 0.5; // dist. junc to SL [um]
// DcaJuncSL = 1.64e-6; // Dca junc to SL [cm^2/sec]
// DcaSLcyto = 1.22e-6; // Dca SL to cyto [cm^2/sec]
// DnaJuncSL = 1.09e-5; // Dna junc to SL [cm^2/sec]
// DnaSLcyto = 1.79e-5; // Dna SL to cyto [cm^2/sec]
Vcell = pi * pow(cellRadius, 2) * cellLength * 1e-15; // [L]
Vmyo = 0.65 * Vcell;
Vsr = 0.035 * Vcell;
Vsl = 0.02 * Vcell;
Vjunc = 0.0539 * 0.01 * Vcell;
// SAjunc = 20150*pi*2*junctionLength*junctionRadius; // [um^2]
// SAsl = pi*2*cellRadius*cellLength; // [um^2]
J_ca_juncsl = 1 / 1.2134e12; // [L/msec]
J_ca_slmyo = 1 / 2.68510e11; // [L/msec]
J_na_juncsl = 1 / (1.6382e12 / 3 * 100); // [L/msec]
J_na_slmyo = 1 / (1.8308e10 / 3 * 100); // [L/msec]
// Fractional currents in compartments
Fjunc = 0.11;
Fsl = 1 - Fjunc;
Fjunc_CaL = 0.9;
Fsl_CaL = 1 - Fjunc_CaL;
// Fixed ion concentrations
Cli = 15; // Intracellular Cl [mM]
Clo = 150; // Extracellular Cl [mM]
Ko = 5.4; // Extracellular K [mM]
Nao = 140; // Extracellular Na [mM]
Cao = 1.8; // Extracellular Ca [mM]
Mgi = 1; // Intracellular Mg [mM]
// Nernst Potentials
ena_junc = (1 / FoRT) * log(Nao / d_initvalu_32); // [mV]
ena_sl = (1 / FoRT) * log(Nao / d_initvalu_33); // [mV]
ek = (1 / FoRT) * log(Ko / d_initvalu_35); // [mV]
eca_junc = (1 / FoRT / 2) * log(Cao / d_initvalu_36); // [mV]
eca_sl = (1 / FoRT / 2) * log(Cao / d_initvalu_37); // [mV]
ecl = (1 / FoRT) * log(Cli / Clo); // [mV]
// Na transport parameters
GNa = 16.0; // [mS/uF]
GNaB = 0.297e-3; // [mS/uF]
IbarNaK = 1.90719; // [uA/uF]
KmNaip = 11; // [mM]
KmKo = 1.5; // [mM]
// Q10NaK = 1.63;
// Q10KmNai = 1.39;
// K current parameters
pNaK = 0.01833;
GtoSlow = 0.06; // [mS/uF]
GtoFast = 0.02; // [mS/uF]
gkp = 0.001;
// Cl current parameters
GClCa = 0.109625; // [mS/uF]
GClB = 9e-3; // [mS/uF]
KdClCa = 100e-3; // [mM]
// I_Ca parameters
pNa = 1.5e-8; // [cm/sec]
pCa = 5.4e-4; // [cm/sec]
pK = 2.7e-7; // [cm/sec]
// KmCa = 0.6e-3; // [mM]
Q10CaL = 1.8;
// Ca transport parameters
IbarNCX = 9.0; // [uA/uF]
KmCai = 3.59e-3; // [mM]
KmCao = 1.3; // [mM]
KmNai = 12.29; // [mM]
KmNao = 87.5; // [mM]
ksat = 0.27; // [none]
nu = 0.35; // [none]
Kdact = 0.256e-3; // [mM]
Q10NCX = 1.57; // [none]
IbarSLCaP = 0.0673; // [uA/uF]
KmPCa = 0.5e-3; // [mM]
GCaB = 2.513e-4; // [uA/uF]
Q10SLCaP = 2.35; // [none]
// SR flux parameters
Q10SRCaP = 2.6; // [none]
Vmax_SRCaP = 2.86e-4; // [mM/msec] (mmol/L cytosol/msec)
Kmf = 0.246e-3; // [mM]
Kmr = 1.7; // [mM]L cytosol
hillSRCaP = 1.787; // [mM]
ks = 25; // [1/ms]
koCa = 10; // [mM^-2 1/ms]
kom = 0.06; // [1/ms]
kiCa = 0.5; // [1/mM/ms]
kim = 0.005; // [1/ms]
ec50SR = 0.45; // [mM]
// Buffering parameters
Bmax_Naj = 7.561; // [mM]
Bmax_Nasl = 1.65; // [mM]
koff_na = 1e-3; // [1/ms]
kon_na = 0.1e-3; // [1/mM/ms]
Bmax_TnClow = 70e-3; // [mM], TnC low affinity
koff_tncl = 19.6e-3; // [1/ms]
kon_tncl = 32.7; // [1/mM/ms]
Bmax_TnChigh = 140e-3; // [mM], TnC high affinity
koff_tnchca = 0.032e-3; // [1/ms]
kon_tnchca = 2.37; // [1/mM/ms]
koff_tnchmg = 3.33e-3; // [1/ms]
kon_tnchmg = 3e-3; // [1/mM/ms]
// Bmax_CaM = 24e-3; // [mM], CaM buffering
// koff_cam = 238e-3; // [1/ms]
// kon_cam = 34; // [1/mM/ms]
Bmax_myosin = 140e-3; // [mM], Myosin buffering
koff_myoca = 0.46e-3; // [1/ms]
kon_myoca = 13.8; // [1/mM/ms]
koff_myomg = 0.057e-3; // [1/ms]
kon_myomg = 0.0157; // [1/mM/ms]
Bmax_SR = 19 * 0.9e-3; // [mM]
koff_sr = 60e-3; // [1/ms]
kon_sr = 100; // [1/mM/ms]
Bmax_SLlowsl = 37.38e-3 * Vmyo / Vsl; // [mM], SL buffering
Bmax_SLlowj = 4.62e-3 * Vmyo / Vjunc * 0.1; // [mM]
koff_sll = 1300e-3; // [1/ms]
kon_sll = 100; // [1/mM/ms]
Bmax_SLhighsl = 13.35e-3 * Vmyo / Vsl; // [mM]
Bmax_SLhighj = 1.65e-3 * Vmyo / Vjunc * 0.1; // [mM]
koff_slh = 30e-3; // [1/ms]
kon_slh = 100; // [1/mM/ms]
Bmax_Csqn = 2.7; // 140e-3*Vmyo/Vsr; [mM]
koff_csqn = 65; // [1/ms]
kon_csqn = 100; // [1/mM/ms]
// I_Na: Fast Na Current
am = 0.32 * (d_initvalu_39 + 47.13) / (1 - exp(-0.1 * (d_initvalu_39 + 47.13)));
bm = 0.08 * exp(-d_initvalu_39 / 11);
if (d_initvalu_39 >= -40) {
ah = 0;
aj = 0;
bh = 1 / (0.13 * (1 + exp(-(d_initvalu_39 + 10.66) / 11.1)));
bj = 0.3 * exp(-2.535e-7 * d_initvalu_39) / (1 + exp(-0.1 * (d_initvalu_39 + 32)));
} else {
ah = 0.135 * exp((80 + d_initvalu_39) / -6.8);
bh = 3.56 * exp(0.079 * d_initvalu_39) + 3.1e5 * exp(0.35 * d_initvalu_39);
aj = (-127140 * exp(0.2444 * d_initvalu_39) - 3.474e-5 * exp(-0.04391 * d_initvalu_39))
* (d_initvalu_39 + 37.78) / (1 + exp(0.311 * (d_initvalu_39 + 79.23)));
bj = 0.1212 * exp(-0.01052 * d_initvalu_39) / (1 + exp(-0.1378 * (d_initvalu_39 + 40.14)));
}
d_finavalu[offset_1] = am * (1 - d_initvalu_1) - bm * d_initvalu_1;
d_finavalu[offset_2] = ah * (1 - d_initvalu_2) - bh * d_initvalu_2;
d_finavalu[offset_3] = aj * (1 - d_initvalu_3) - bj * d_initvalu_3;
I_Na_junc = Fjunc * GNa * pow(d_initvalu_1, 3) * d_initvalu_2 * d_initvalu_3
* (d_initvalu_39 - ena_junc);
I_Na_sl = Fsl * GNa * pow(d_initvalu_1, 3) * d_initvalu_2 * d_initvalu_3
* (d_initvalu_39 - ena_sl);
// I_Na = I_Na_junc+I_Na_sl;
// I_nabk: Na Background Current
I_nabk_junc = Fjunc * GNaB * (d_initvalu_39 - ena_junc);
I_nabk_sl = Fsl * GNaB * (d_initvalu_39 - ena_sl);
// I_nabk = I_nabk_junc+I_nabk_sl;
// I_nak: Na/K Pump Current
sigma = (exp(Nao / 67.3) - 1) / 7;
fnak =
1
/ (1 + 0.1245 * exp(-0.1 * d_initvalu_39 * FoRT)
+ 0.0365 * sigma * exp(-d_initvalu_39 * FoRT));
I_nak_junc = Fjunc * IbarNaK * fnak * Ko / (1 + pow((KmNaip / d_initvalu_32), 4)) / (Ko + KmKo);
I_nak_sl = Fsl * IbarNaK * fnak * Ko / (1 + pow((KmNaip / d_initvalu_33), 4)) / (Ko + KmKo);
I_nak = I_nak_junc + I_nak_sl;
// I_kr: Rapidly Activating K Current
gkr = 0.03 * sqrt(Ko / 5.4);
xrss = 1 / (1 + exp(-(d_initvalu_39 + 50) / 7.5));
tauxr = 1
/ (0.00138 * (d_initvalu_39 + 7) / (1 - exp(-0.123 * (d_initvalu_39 + 7)))
+ 6.1e-4 * (d_initvalu_39 + 10) / (exp(0.145 * (d_initvalu_39 + 10)) - 1));
d_finavalu[offset_12] = (xrss - d_initvalu_12) / tauxr;
rkr = 1 / (1 + exp((d_initvalu_39 + 33) / 22.4));
I_kr = gkr * d_initvalu_12 * rkr * (d_initvalu_39 - ek);
// I_ks: Slowly Activating K Current
pcaks_junc = -log10(d_initvalu_36) + 3.0;
pcaks_sl = -log10(d_initvalu_37) + 3.0;
gks_junc = 0.07 * (0.057 + 0.19 / (1 + exp((-7.2 + pcaks_junc) / 0.6)));
gks_sl = 0.07 * (0.057 + 0.19 / (1 + exp((-7.2 + pcaks_sl) / 0.6)));
eks = (1 / FoRT) * log((Ko + pNaK * Nao) / (d_initvalu_35 + pNaK * d_initvalu_34));
xsss = 1 / (1 + exp(-(d_initvalu_39 - 1.5) / 16.7));
tauxs = 1
/ (7.19e-5 * (d_initvalu_39 + 30) / (1 - exp(-0.148 * (d_initvalu_39 + 30)))
+ 1.31e-4 * (d_initvalu_39 + 30) / (exp(0.0687 * (d_initvalu_39 + 30)) - 1));
d_finavalu[offset_13] = (xsss - d_initvalu_13) / tauxs;
I_ks_junc = Fjunc * gks_junc * pow(d_initvalu_12, 2) * (d_initvalu_39 - eks);
I_ks_sl = Fsl * gks_sl * pow(d_initvalu_13, 2) * (d_initvalu_39 - eks);
I_ks = I_ks_junc + I_ks_sl;
// I_kp: Plateau K current
kp_kp = 1 / (1 + exp(7.488 - d_initvalu_39 / 5.98));
I_kp_junc = Fjunc * gkp * kp_kp * (d_initvalu_39 - ek);
I_kp_sl = Fsl * gkp * kp_kp * (d_initvalu_39 - ek);
I_kp = I_kp_junc + I_kp_sl;
// I_to: Transient Outward K Current (slow and fast components)
xtoss = 1 / (1 + exp(-(d_initvalu_39 + 3.0) / 15));
ytoss = 1 / (1 + exp((d_initvalu_39 + 33.5) / 10));
rtoss = 1 / (1 + exp((d_initvalu_39 + 33.5) / 10));
tauxtos = 9 / (1 + exp((d_initvalu_39 + 3.0) / 15)) + 0.5;
tauytos = 3e3 / (1 + exp((d_initvalu_39 + 60.0) / 10)) + 30;
taurtos = 2800 / (1 + exp((d_initvalu_39 + 60.0) / 10)) + 220;
d_finavalu[offset_8] = (xtoss - d_initvalu_8) / tauxtos;
d_finavalu[offset_9] = (ytoss - d_initvalu_9) / tauytos;
d_finavalu[offset_40] = (rtoss - d_initvalu_40) / taurtos;
I_tos = GtoSlow * d_initvalu_8 * (d_initvalu_9 + 0.5 * d_initvalu_40) * (d_initvalu_39 - ek); // [uA/uF]
//
tauxtof = 3.5 * exp(-d_initvalu_39 * d_initvalu_39 / 30 / 30) + 1.5;
tauytof = 20.0 / (1 + exp((d_initvalu_39 + 33.5) / 10)) + 20.0;
d_finavalu[offset_10] = (xtoss - d_initvalu_10) / tauxtof;
d_finavalu[offset_11] = (ytoss - d_initvalu_11) / tauytof;
I_tof = GtoFast * d_initvalu_10 * d_initvalu_11 * (d_initvalu_39 - ek);
I_to = I_tos + I_tof;
// I_ki: Time-Independent K Current
aki = 1.02 / (1 + exp(0.2385 * (d_initvalu_39 - ek - 59.215)));
bki = (0.49124 * exp(0.08032 * (d_initvalu_39 + 5.476 - ek))
+ exp(0.06175 * (d_initvalu_39 - ek - 594.31)))
/ (1 + exp(-0.5143 * (d_initvalu_39 - ek + 4.753)));
kiss = aki / (aki + bki);
I_ki = 0.9 * sqrt(Ko / 5.4) * kiss * (d_initvalu_39 - ek);
// I_ClCa: Ca-activated Cl Current, I_Clbk: background Cl Current
I_ClCa_junc = Fjunc * GClCa / (1 + KdClCa / d_initvalu_36) * (d_initvalu_39 - ecl);
I_ClCa_sl = Fsl * GClCa / (1 + KdClCa / d_initvalu_37) * (d_initvalu_39 - ecl);
I_ClCa = I_ClCa_junc + I_ClCa_sl;
I_Clbk = GClB * (d_initvalu_39 - ecl);
// I_Ca: L-type Calcium Current
dss = 1 / (1 + exp(-(d_initvalu_39 + 14.5) / 6.0));
taud = dss * (1 - exp(-(d_initvalu_39 + 14.5) / 6.0)) / (0.035 * (d_initvalu_39 + 14.5));
fss = 1 / (1 + exp((d_initvalu_39 + 35.06) / 3.6)) + 0.6 / (1 + exp((50 - d_initvalu_39) / 20));
tauf = 1 / (0.0197 * exp(-pow(0.0337 * (d_initvalu_39 + 14.5), 2)) + 0.02);
d_finavalu[offset_4] = (dss - d_initvalu_4) / taud;
d_finavalu[offset_5] = (fss - d_initvalu_5) / tauf;
d_finavalu[offset_6] = 1.7 * d_initvalu_36 * (1 - d_initvalu_6) - 11.9e-3 * d_initvalu_6; // fCa_junc
d_finavalu[offset_7] = 1.7 * d_initvalu_37 * (1 - d_initvalu_7) - 11.9e-3 * d_initvalu_7; // fCa_sl
//
ibarca_j = pCa * 4 * (d_initvalu_39 * Frdy * FoRT)
* (0.341 * d_initvalu_36 * exp(2 * d_initvalu_39 * FoRT) - 0.341 * Cao)
/ (exp(2 * d_initvalu_39 * FoRT) - 1);
ibarca_sl = pCa * 4 * (d_initvalu_39 * Frdy * FoRT)
* (0.341 * d_initvalu_37 * exp(2 * d_initvalu_39 * FoRT) - 0.341 * Cao)
/ (exp(2 * d_initvalu_39 * FoRT) - 1);
ibark = pK * (d_initvalu_39 * Frdy * FoRT)
* (0.75 * d_initvalu_35 * exp(d_initvalu_39 * FoRT) - 0.75 * Ko)
/ (exp(d_initvalu_39 * FoRT) - 1);
ibarna_j = pNa * (d_initvalu_39 * Frdy * FoRT)
* (0.75 * d_initvalu_32 * exp(d_initvalu_39 * FoRT) - 0.75 * Nao)
/ (exp(d_initvalu_39 * FoRT) - 1);
ibarna_sl = pNa * (d_initvalu_39 * Frdy * FoRT)
* (0.75 * d_initvalu_33 * exp(d_initvalu_39 * FoRT) - 0.75 * Nao)
/ (exp(d_initvalu_39 * FoRT) - 1);
I_Ca_junc = (Fjunc_CaL * ibarca_j * d_initvalu_4 * d_initvalu_5 * (1 - d_initvalu_6)
* pow(Q10CaL, Qpow)) * 0.45;
I_Ca_sl = (Fsl_CaL * ibarca_sl * d_initvalu_4 * d_initvalu_5 * (1 - d_initvalu_7)
* pow(Q10CaL, Qpow)) * 0.45;
I_Ca = I_Ca_junc + I_Ca_sl;
d_finavalu[offset_43] = -I_Ca * Cmem / (Vmyo * 2 * Frdy) * 1e3;
I_CaK = (ibark * d_initvalu_4 * d_initvalu_5
* (Fjunc_CaL * (1 - d_initvalu_6) + Fsl_CaL * (1 - d_initvalu_7)) * pow(Q10CaL, Qpow)) * 0.45;
I_CaNa_junc = (Fjunc_CaL * ibarna_j * d_initvalu_4 * d_initvalu_5 * (1 - d_initvalu_6)
* pow(Q10CaL, Qpow)) * 0.45;
I_CaNa_sl = (Fsl_CaL * ibarna_sl * d_initvalu_4 * d_initvalu_5 * (1 - d_initvalu_7)
* pow(Q10CaL, Qpow)) * 0.45;
// I_CaNa = I_CaNa_junc+I_CaNa_sl;
// I_Catot = I_Ca+I_CaK+I_CaNa;
// I_ncx: Na/Ca Exchanger flux
Ka_junc = 1 / (1 + pow((Kdact / d_initvalu_36), 3));
Ka_sl = 1 / (1 + pow((Kdact / d_initvalu_37), 3));
s1_junc = exp(nu * d_initvalu_39 * FoRT) * pow(d_initvalu_32, 3) * Cao;
s1_sl = exp(nu * d_initvalu_39 * FoRT) * pow(d_initvalu_33, 3) * Cao;
s2_junc = exp((nu - 1) * d_initvalu_39 * FoRT) * pow(Nao, 3) * d_initvalu_36;
s3_junc = (KmCai * pow(Nao, 3) * (1 + pow((d_initvalu_32 / KmNai), 3))
+ pow(KmNao, 3) * d_initvalu_36 + pow(KmNai, 3) * Cao * (1 + d_initvalu_36 / KmCai)
+ KmCao * pow(d_initvalu_32, 3) + pow(d_initvalu_32, 3) * Cao + pow(Nao, 3) * d_initvalu_36)
* (1 + ksat * exp((nu - 1) * d_initvalu_39 * FoRT));
s2_sl = exp((nu - 1) * d_initvalu_39 * FoRT) * pow(Nao, 3) * d_initvalu_37;
s3_sl = (KmCai * pow(Nao, 3) * (1 + pow((d_initvalu_33 / KmNai), 3))
+ pow(KmNao, 3) * d_initvalu_37 + pow(KmNai, 3) * Cao * (1 + d_initvalu_37 / KmCai)
+ KmCao * pow(d_initvalu_33, 3) + pow(d_initvalu_33, 3) * Cao + pow(Nao, 3) * d_initvalu_37)
* (1 + ksat * exp((nu - 1) * d_initvalu_39 * FoRT));
I_ncx_junc = Fjunc * IbarNCX * pow(Q10NCX, Qpow) * Ka_junc * (s1_junc - s2_junc) / s3_junc;
I_ncx_sl = Fsl * IbarNCX * pow(Q10NCX, Qpow) * Ka_sl * (s1_sl - s2_sl) / s3_sl;
I_ncx = I_ncx_junc + I_ncx_sl;
d_finavalu[offset_45] = 2 * I_ncx * Cmem / (Vmyo * 2 * Frdy) * 1e3;
// I_pca: Sarcolemmal Ca Pump Current
I_pca_junc = Fjunc * pow(Q10SLCaP, Qpow) * IbarSLCaP * pow(d_initvalu_36, float(1.6))
/ (pow(KmPCa, float(1.6)) + pow(d_initvalu_36, float(1.6)));
I_pca_sl = Fsl * pow(Q10SLCaP, Qpow) * IbarSLCaP * pow(d_initvalu_37, float(1.6))
/ (pow(KmPCa, float(1.6)) + pow(d_initvalu_37, float(1.6)));
I_pca = I_pca_junc + I_pca_sl;
d_finavalu[offset_44] = -I_pca * Cmem / (Vmyo * 2 * Frdy) * 1e3;
// I_cabk: Ca Background Current
I_cabk_junc = Fjunc * GCaB * (d_initvalu_39 - eca_junc);
I_cabk_sl = Fsl * GCaB * (d_initvalu_39 - eca_sl);
I_cabk = I_cabk_junc + I_cabk_sl;
d_finavalu[offset_46] = -I_cabk * Cmem / (Vmyo * 2 * Frdy) * 1e3;
// SR fluxes: Calcium Release, SR Ca pump, SR Ca leak
MaxSR = 15;
MinSR = 1;
kCaSR = MaxSR - (MaxSR - MinSR) / (1 + pow(ec50SR / d_initvalu_31, float(2.5)));
koSRCa = koCa / kCaSR;
kiSRCa = kiCa * kCaSR;
RI = 1 - d_initvalu_14 - d_initvalu_15 - d_initvalu_16;
d_finavalu[offset_14] = (kim * RI - kiSRCa * d_initvalu_36 * d_initvalu_14)
- (koSRCa * pow(d_initvalu_36, 2) * d_initvalu_14 - kom * d_initvalu_15); // R
d_finavalu[offset_15] = (koSRCa * pow(d_initvalu_36, 2) * d_initvalu_14 - kom * d_initvalu_15)
- (kiSRCa * d_initvalu_36 * d_initvalu_15 - kim * d_initvalu_16); // O
d_finavalu[offset_16] = (kiSRCa * d_initvalu_36 * d_initvalu_15 - kim * d_initvalu_16)
- (kom * d_initvalu_16 - koSRCa * pow(d_initvalu_36, 2) * RI); // I
J_SRCarel = ks * d_initvalu_15 * (d_initvalu_31 - d_initvalu_36); // [mM/ms]
J_serca = pow(Q10SRCaP, Qpow) * Vmax_SRCaP
* (pow((d_initvalu_38 / Kmf), hillSRCaP) - pow((d_initvalu_31 / Kmr), hillSRCaP))
/ (1 + pow((d_initvalu_38 / Kmf), hillSRCaP) + pow((d_initvalu_31 / Kmr), hillSRCaP));
J_SRleak = 5.348e-6 * (d_initvalu_31 - d_initvalu_36); // [mM/ms]
// Sodium and Calcium Buffering
d_finavalu[offset_17] = kon_na * d_initvalu_32 * (Bmax_Naj - d_initvalu_17)
- koff_na * d_initvalu_17; // NaBj [mM/ms]
d_finavalu[offset_18] = kon_na * d_initvalu_33 * (Bmax_Nasl - d_initvalu_18)
- koff_na * d_initvalu_18; // NaBsl [mM/ms]
// Cytosolic Ca Buffers
d_finavalu[offset_19] = kon_tncl * d_initvalu_38 * (Bmax_TnClow - d_initvalu_19)
- koff_tncl * d_initvalu_19; // TnCL [mM/ms]
d_finavalu[offset_20] = kon_tnchca * d_initvalu_38
* (Bmax_TnChigh - d_initvalu_20 - d_initvalu_21) - koff_tnchca * d_initvalu_20; // TnCHc [mM/ms]
d_finavalu[offset_21] = kon_tnchmg * Mgi * (Bmax_TnChigh - d_initvalu_20 - d_initvalu_21)
- koff_tnchmg * d_initvalu_21; // TnCHm [mM/ms]
d_finavalu[offset_22] = 0; // CaM [mM/ms]
d_finavalu[offset_23] = kon_myoca * d_initvalu_38 * (Bmax_myosin - d_initvalu_23 - d_initvalu_24)
- koff_myoca * d_initvalu_23; // Myosin_ca [mM/ms]
d_finavalu[offset_24] = kon_myomg * Mgi * (Bmax_myosin - d_initvalu_23 - d_initvalu_24)
- koff_myomg * d_initvalu_24; // Myosin_mg [mM/ms]
d_finavalu[offset_25] = kon_sr * d_initvalu_38 * (Bmax_SR - d_initvalu_25)
- koff_sr * d_initvalu_25; // SRB [mM/ms]
J_CaB_cytosol = d_finavalu[offset_19] + d_finavalu[offset_20] + d_finavalu[offset_21]
+ d_finavalu[offset_22] + d_finavalu[offset_23] + d_finavalu[offset_24]
+ d_finavalu[offset_25];
// Junctional and SL Ca Buffers
d_finavalu[offset_26] = kon_sll * d_initvalu_36 * (Bmax_SLlowj - d_initvalu_26)
- koff_sll * d_initvalu_26; // SLLj [mM/ms]
d_finavalu[offset_27] = kon_sll * d_initvalu_37 * (Bmax_SLlowsl - d_initvalu_27)
- koff_sll * d_initvalu_27; // SLLsl [mM/ms]
d_finavalu[offset_28] = kon_slh * d_initvalu_36 * (Bmax_SLhighj - d_initvalu_28)
- koff_slh * d_initvalu_28; // SLHj [mM/ms]
d_finavalu[offset_29] = kon_slh * d_initvalu_37 * (Bmax_SLhighsl - d_initvalu_29)
- koff_slh * d_initvalu_29; // SLHsl [mM/ms]
J_CaB_junction = d_finavalu[offset_26] + d_finavalu[offset_28];
J_CaB_sl = d_finavalu[offset_27] + d_finavalu[offset_29];
// SR Ca Concentrations
d_finavalu[offset_30] = kon_csqn * d_initvalu_31 * (Bmax_Csqn - d_initvalu_30)
- koff_csqn * d_initvalu_30; // Csqn [mM/ms]
oneovervsr = 1 / Vsr;
d_finavalu[offset_31] = J_serca * Vmyo * oneovervsr - (J_SRleak * Vmyo * oneovervsr + J_SRCarel)
- d_finavalu[offset_30]; // Ca_sr [mM/ms] %Ratio 3 leak current
// Sodium Concentrations
I_Na_tot_junc = I_Na_junc + I_nabk_junc + 3 * I_ncx_junc + 3 * I_nak_junc + I_CaNa_junc;// [uA/uF]
I_Na_tot_sl = I_Na_sl + I_nabk_sl + 3 * I_ncx_sl + 3 * I_nak_sl + I_CaNa_sl; // [uA/uF]
d_finavalu[offset_32] = -I_Na_tot_junc * Cmem / (Vjunc * Frdy)
+ J_na_juncsl / Vjunc * (d_initvalu_33 - d_initvalu_32) - d_finavalu[offset_17];
oneovervsl = 1 / Vsl;
d_finavalu[offset_33] = -I_Na_tot_sl * Cmem * oneovervsl / Frdy
+ J_na_juncsl * oneovervsl * (d_initvalu_32 - d_initvalu_33)
+ J_na_slmyo * oneovervsl * (d_initvalu_34 - d_initvalu_33) - d_finavalu[offset_18];
d_finavalu[offset_34] = J_na_slmyo / Vmyo * (d_initvalu_33 - d_initvalu_34); // [mM/msec]
// Potassium Concentration
I_K_tot = I_to + I_kr + I_ks + I_ki - 2 * I_nak + I_CaK + I_kp; // [uA/uF]
d_finavalu[offset_35] = 0; // [mM/msec]
// Calcium Concentrations
I_Ca_tot_junc = I_Ca_junc + I_cabk_junc + I_pca_junc - 2 * I_ncx_junc; // [uA/uF]
I_Ca_tot_sl = I_Ca_sl + I_cabk_sl + I_pca_sl - 2 * I_ncx_sl; // [uA/uF]
d_finavalu[offset_36] = -I_Ca_tot_junc * Cmem / (Vjunc * 2 * Frdy)
+ J_ca_juncsl / Vjunc * (d_initvalu_37 - d_initvalu_36) - J_CaB_junction
+ (J_SRCarel) * Vsr / Vjunc + J_SRleak * Vmyo / Vjunc; // Ca_j
d_finavalu[offset_37] = -I_Ca_tot_sl * Cmem / (Vsl * 2 * Frdy)
+ J_ca_juncsl / Vsl * (d_initvalu_36 - d_initvalu_37)
+ J_ca_slmyo / Vsl * (d_initvalu_38 - d_initvalu_37) - J_CaB_sl; // Ca_sl
d_finavalu[offset_38] = -J_serca - J_CaB_cytosol
+ J_ca_slmyo / Vmyo * (d_initvalu_37 - d_initvalu_38);
// junc_sl=J_ca_juncsl/Vsl*(d_initvalu_36-d_initvalu_37);
// sl_junc=J_ca_juncsl/Vjunc*(d_initvalu_37-d_initvalu_36);
// sl_myo=J_ca_slmyo/Vsl*(d_initvalu_38-d_initvalu_37);
// myo_sl=J_ca_slmyo/Vmyo*(d_initvalu_37-d_initvalu_38);
// Simulation type
state = 1;
switch (state) {
case 0:
I_app = 0;
break;
case 1: // pace w/ current injection at cycleLength 'cycleLength'
if (fmod(timeinst, cycleLength) <= 5) {
I_app = 9.5;
} else {
I_app = 0.0;
}
break;
case 2:
V_hold = -55;
V_test = 0;
if (timeinst > 0.5 & timeinst < 200.5) {
V_clamp = V_test;
} else {
V_clamp = V_hold;
}
R_clamp = 0.04;
I_app = (V_clamp - d_initvalu_39) / R_clamp;
break;
}
// Membrane Potential
I_Na_tot = I_Na_tot_junc + I_Na_tot_sl; // [uA/uF]
I_Cl_tot = I_ClCa + I_Clbk; // [uA/uF]
I_Ca_tot = I_Ca_tot_junc + I_Ca_tot_sl;
I_tot = I_Na_tot + I_Cl_tot + I_Ca_tot + I_K_tot;
d_finavalu[offset_39] = -(I_tot - I_app);
// Set unused output values to 0 (MATLAB does it by default)
d_finavalu[offset_41] = 0;
d_finavalu[offset_42] = 0;
}
|
18,757 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include "curand.h"
#include <iostream>
#include <iomanip>
using namespace std;
__device__ int dCount = 0;
__global__ void countPoints(const float* xs, const float* ys)
{
int idx = blockIdx.x * blockDim.x + threadIdx.x;
float x = xs[idx] - 0.5f;
float y = ys[idx] - 0.5f;
int n = sqrtf(x*x + y*y) > 0.5f ? 0 : 1;
atomicAdd(&dCount, n);
}
int main(int argc, char* argv[])
{
const int count = 512*512;
const int size = count * sizeof(float);
cudaError_t cudaStatus;
curandStatus_t curandStatus;
curandGenerator_t gen;
curandStatus = curandCreateGenerator(&gen, CURAND_RNG_PSEUDO_MTGP32);
curandSetPseudoRandomGeneratorSeed(gen, time(0));
float *x;
float *y;
cudaStatus = cudaMalloc((void**)&x, size);
cudaStatus = cudaMalloc((void**)&y, size);
curandStatus = curandGenerateUniform(gen, x, count);
curandStatus = curandGenerateUniform(gen, y, count);
countPoints<<<512,512>>>(x, y);
cudaDeviceSynchronize();
int hCount;
cudaMemcpyFromSymbol(&hCount, dCount, sizeof(int));
cudaFree(x);
cudaFree(y);
cudaDeviceReset();
cout << setprecision(12)
<< "Pi is approximately "
<< (4.0f * (float)hCount / (float)count)
<< endl;
getchar();
return 0;
} |
18,758 | /*
* Hello world for CUDA, with access to the shared memory of the multiprocessors
*/
#include <stdio.h>
#include <stdlib.h>
__shared__ float sums[10];
// Define a kernel function
__global__ void vector_sum(float* A, float* B, int length, const int N) {
// Take a vector A of length "length" and sum it, putting the result in
// the vector B
// Declare some shared memory to store the sums.
// We need enough floats for each thread to have one
//__shared__ float sums[N];
// Our ID is unique to our thread, so use it as our index
// Initialise our sum
sums[threadIdx.x] = 0;
// Calculate the sum
for (unsigned int i = 0; i < length; i++) {
sums[threadIdx.x] += A[i];
}
B[threadIdx.x] = sums[threadIdx.x];
}
int main() {
// This is the size of our output vector, and the number of threads
const int N = 10;
// This will be the length of our input vectors
int length = 50;
// These will be our vectors on the host
float* host_A; // This contains all input vectors
float* host_B;
// Use this for indices
int i;
// Define our vectors on the host
host_A = (float*) malloc(N*length*sizeof(float));
host_B = (float*) malloc(N*sizeof(float));
// Initialise them
for (i = 0; i < N*length; i++) {
host_A[i] = (float)(i%length);
//host_B[i] = 0.0;
}
// Define our vectors on the GPU
float* device_A;
float* device_B;
cudaMalloc((void**) &device_A, sizeof(float)*N*length);
cudaMalloc((void**) &device_B, sizeof(float)*N);
// Transfer data to the GPU
cudaMemcpy(device_A, host_A, sizeof(float)*N*length,
cudaMemcpyHostToDevice);
//cudaMemcpy(device_B, host_B, sizeof(float)*N, cudaMemcpyHostToDevice);
// Call our function; second number is how many threads to use
// The first number is to do with thread blocks...
vector_sum<<<1, N>>>(device_A, device_B, length, N);
// Copy memory back
cudaMemcpy(host_B, device_B, sizeof(float)*N, cudaMemcpyDeviceToHost);
// Free device memory
cudaFree(device_A);
cudaFree(device_B);
// Output our results
printf("A = [");
for (i = 0; i < N*length; i++) {
if (i%length == 0) {
printf("\n");
}
printf("%G,", host_A[i]);
}
printf("]\n");
printf("B = [");
for (i = 0; i < N; i++) {
printf("%G,", host_B[i]);
}
printf("]\n");
return 0;
}
|
18,759 | #include "includes.h"
__global__ void hotspotOpt1(float *p, float* tIn, float *tOut, float sdc, int nx, int ny, int nz, float ce, float cw, float cn, float cs, float ct, float cb, float cc)
{
float amb_temp = 80.0;
int i = blockDim.x * blockIdx.x + threadIdx.x;
int j = blockDim.y * blockIdx.y + threadIdx.y;
int c = i + j * nx;
int xy = nx * ny;
int W = (i == 0) ? c : c - 1;
int E = (i == nx-1) ? c : c + 1;
int N = (j == 0) ? c : c - nx;
int S = (j == ny-1) ? c : c + nx;
float temp1, temp2, temp3;
temp1 = temp2 = tIn[c];
temp3 = tIn[c+xy];
tOut[c] = cc * temp2 + cw * tIn[W] + ce * tIn[E] + cs * tIn[S]
+ cn * tIn[N] + cb * temp1 + ct * temp3 + sdc * p[c] + ct * amb_temp;
c += xy;
W += xy;
E += xy;
N += xy;
S += xy;
for (int k = 1; k < nz-1; ++k) {
temp1 = temp2;
temp2 = temp3;
temp3 = tIn[c+xy];
tOut[c] = cc * temp2 + cw * tIn[W] + ce * tIn[E] + cs * tIn[S]
+ cn * tIn[N] + cb * temp1 + ct * temp3 + sdc * p[c] + ct * amb_temp;
c += xy;
W += xy;
E += xy;
N += xy;
S += xy;
}
temp1 = temp2;
temp2 = temp3;
tOut[c] = cc * temp2 + cw * tIn[W] + ce * tIn[E] + cs * tIn[S]
+ cn * tIn[N] + cb * temp1 + ct * temp3 + sdc * p[c] + ct * amb_temp;
return;
} |
18,760 | // System includes
#include <stdio.h>
#include <cuda_runtime.h>
#include<device_launch_parameters.h>
#include<curand.h>
#define _USE_MATH_DEFINES
#include<math.h>
__device__ __host__ __inline__ float N(float x)
{
return 0.5 + 0.5 * erf(x * M_SQRT1_2);
}
__device__ __host__ void price(float k, float s, float t, float r, float v, float *c, float* p) {
float srt = v * sqrtf(t);
float d1 = (logf(s/k) + (r + 0.5 * v * v) * t) /srt;
float d2 = d1 - srt;
float kert = k * expf(-r * t);
*c = N(d1) * s - N(d2) * kert;
*p = kert - s + *c;
}
__global__ void price(float* k, float* s, float* t, float* r, float *v, float* c, float *p)
{
int idx = threadIdx.x;
price(k[idx], s[idx], t[idx], r[idx], v[idx], c + idx, p + idx);
}
int scatter()
{
const int count = 512;
float *args[5];
const int size = count * sizeof(float);
curandGenerator_t gen;
curandCreateGenerator(&gen, CURAND_RNG_PSEUDO_MTGP32);
for(int i = 0 ; i < 5 ; i ++)
{
cudaMalloc(args + i, size);
curandGenerateUniform(gen, args[i], count);
}
float *dc, *dp;
cudaMalloc(&dc, size);
cudaMalloc(&dp, size);
price<<<1, count>>>(args[0], args[1], args[2], args[3], args[4], dc, dp);
float *hc, *hp;
hc = (float*)malloc(size);
hp = (float*)malloc(size);
cudaMemcpy(hc, dc, size, cudaMemcpyDeviceToHost);
cudaMemcpy(hp, dp, size, cudaMemcpyDeviceToHost);
for(int i = 0 ; i < 512 ; i ++) {
printf("Element %d has c = %f and p = %f\n", i, hc[i], hp[i]);
}
free(hc);
free(hp);
cudaFree(dc);
cudaFree(dp);
return 0;
}
|
18,761 | /*
*
* Authors: Steven Faulkner, Blaine Oakley, Felipe Gutierrez
*
* Final Project for CIS 4930, Implementation of K-means clustering
* optimized with shared memory and reduction methods.
*
* To compile nvcc kmeans.cu
* To run ./a.out "input.txt" "K" "iterations"
*
* @data file: is the specified input file
* @k: is the number of centroids tro be determined
* @iterations: total number of iterations to be performed
*
*/
#include<cuda_runtime.h>
#include <cstdlib>
#include <ctime>
#include <algorithm>
#include <fstream>
#include <iostream>
#include <sstream>
#include <vector>
#include <string>
void ErrorCheck( cudaError_t err, const char op[]){
/*
* @cudaError_t err: cuda api call catcher.
* * all cuda api's usually return
* cudaSuccess
* @const char op[]: error string will tell where api call
* failed
*
* Error Catch Function, will wrap all malloc, memset and
* memcopy calls
*
*/
if( err != cudaSuccess )
{
printf("CUDA Error: %s, %s ", op, cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
}
struct Point {
/*
*
* Struct for the imported data points
*
* Follows CADRe stanadards, moved cuadaMalloc and cudaFree
* to the constructors, destrucor. Just makes the code look
* a little cleaner
*
*
* @double x: x data point
* @double y: y data point
* @int size: size ofdata point
* @int bytes: # of bytes allocated for storage
*/
Point(long dataSize) : dataSize(dataSize), num_of_bytes(dataSize * sizeof(double)) {
ErrorCheck(cudaMalloc(&x, num_of_bytes),"Allocate x data\n");
ErrorCheck(cudaMalloc(&y, num_of_bytes), "Allocate y data\n");
ErrorCheck(cudaMemset(x, 0, num_of_bytes), "Set x data to '0'\n");
ErrorCheck(cudaMemset(y, 0, num_of_bytes), "Set y data to '0'\n");
}
Point(long dataSize, std::vector<double>& x_data, std::vector<double>& y_data) : dataSize(dataSize), num_of_bytes(dataSize * sizeof(double)) {
ErrorCheck(cudaMalloc(&x, num_of_bytes),"Allocate x array\n");
ErrorCheck(cudaMalloc(&y, num_of_bytes), "Allocate y array\n");;
ErrorCheck(cudaMemcpy(x, x_data.data(), num_of_bytes, cudaMemcpyHostToDevice),"Copy x array to device\n");
ErrorCheck(cudaMemcpy(y, y_data.data(), num_of_bytes, cudaMemcpyHostToDevice), "Copy y array to device\n");
}
~Point() {
ErrorCheck(cudaFree(x),"Freeing x \n");
ErrorCheck(cudaFree(y),"Freeing y \n");
}
double* x;
double* y;
long dataSize;
int num_of_bytes;
};
__device__ double
euclidean_distance(double x_1, double y_1, double x_2, double y_2) {
/*
*
* @double x_1, y_1, x_2, y_2: x and y coordinates from Point struct
*
*
* Standard Euclidean Distance function returns a straight line distance
* Point A to Point B.
*
* //If I Have Time\\
* We can exapnd this for higher dimensional data(add more x_n - x_m) or preprocess our data
* with PCA(prinicpal component analysis) to reduce to 2 dimensions
*
*/
return sqrt((x_1 - x_2) * (x_1 - x_2) + (y_1 - y_2) * (y_1 - y_2));
}
__global__ void
Assignment(double * data_x, double * data_y, int data_size, double * centroids_x, double * centroids_y, double * device_new_x, double * device_new_y, int k, int * device_counts) {
/*
*
* @double* data_x: array of x data points
* @double* data_y: array of y data points
* @int data_size: size of data array
* @centroid_x: array of x centroids
* @centroid_y: array of y centroids
* @device_new_x: updated array for x
* @device_new_y: updated array for y
* @int k: # of centroids
* @int* device_counts: int array, holds count
* for total points among all centodsi
*
* K-Means Algorithm : each x,y cluster is assigned to its closest centroid
* then each centroid is averaged over all the points
* assigned to it and then updated with this new value
*/
extern __shared__ double shared_mem[];
int reg = threadIdx.x;
int unique_index = blockIdx.x * blockDim.x + threadIdx.x;
//out of range
if (unique_index >= data_size) return;
//loading in centroids
if (reg < k) {
//1D array seperated by K values for each point
shared_mem[reg] = centroids_x[reg];
shared_mem[k + reg] = centroids_y[reg];
}
__syncthreads();
// load to registers
double x_value = data_x[unique_index];
double y_value = data_y[unique_index];
//none of our distance values will be large enough to use stl::infinity or FLT_MAX,
// arbitrary sentinal values will suffice for these two variables
double min_distance = 1000;
int best_cluster = -1;
//iterate over the all centroids keeping track of closest one for storage
for (int cluster = 0; cluster < k; ++cluster) {
double distance = euclidean_distance(x_value, y_value, shared_mem[cluster], shared_mem[k + cluster]);
if (distance < min_distance) {
min_distance = distance;
best_cluster = cluster;
}
}
__syncthreads();
// tree-reduction start
int x = reg;
int y = reg + blockDim.x;
int count = reg + blockDim.x + blockDim.x;
//check if thread is assigned to centroid, writing to local memory if true or 0 if false
for (int cluster = 0; cluster < k; ++cluster) {
shared_mem[x] = (best_cluster == cluster) ? x_value : 0;
shared_mem[y] = (best_cluster == cluster) ? y_value : 0;
shared_mem[count] = (best_cluster == cluster) ? 1 : 0;
__syncthreads();
// Reduction for local memory.
for (int stride = blockDim.x / 2; stride > 0; stride /= 2) {
if (reg < stride) {
shared_mem[x] += shared_mem[x + stride];
shared_mem[y] += shared_mem[y + stride];
shared_mem[count] += shared_mem[count + stride];
}
__syncthreads();
}
//push_back from shared mem to update array
if (reg == 0) {
int cluster_index = blockIdx.x * k + cluster;
device_new_x[cluster_index] = shared_mem[x];
device_new_y[cluster_index] = shared_mem[y];
device_counts[cluster_index] = shared_mem[count];
}
__syncthreads();
}
}
__global__ void
centroid_recompute(double * centroids_x, double * centroids_y, double * device_new_x, double * device_new_y, int k, int * device_counts) {
/*
*
*
* @double * centroids_x: array for x centroids
* @double * centroids_y: array for y centroids
* @double * new_sums_x: updated x array
* @double * new_sums_y: updated y array
* @int k: # of centroids
* @int * device_counts: int array,holds count
* for total points among all centodsi
*
*
*
* centroid Recompute: Recomputes the centroids from all
* points assigned to it.
*
*
*/
//local memory declaration
extern __shared__ double shared_mem[];
int reg = threadIdx.x;
int b_Dim = blockDim.x;
//load into local memory
shared_mem[reg] = device_new_x[reg];
shared_mem[b_Dim + reg] = device_new_y[reg];
__syncthreads();
//summination of every stride length block
for (int stride = blockDim.x / 2; stride >= k; stride /= 2) {
if (reg < stride) {
shared_mem[reg] += shared_mem[reg + stride];
shared_mem[b_Dim + reg] += shared_mem[b_Dim + reg + stride];
}
__syncthreads();
}
//recomputing centroid centers
if (reg < k) {
int count = max(1, device_counts[reg]);
centroids_x[reg] = device_new_x[reg] / count;
centroids_y[reg] = device_new_y[reg] / count;
device_new_y[reg] = 0;
device_new_x[reg] = 0;
device_counts[reg] = 0;
}
}
int main(int argc, const char * argv[]) {
if (argc < 4) {
std::cout << "Incorrect startup execution: <./a.out 'input.txt' 'K' 'iterations' " << std::endl;
std::exit(EXIT_FAILURE);
}
int k = std::atoi(argv[2]);
int number_of_iterations = std::atoi(argv[3]);
std::vector<double> x_data;
std::vector<double> y_data;
std::ifstream stream_in(argv[1]);
std::string line;
if(stream_in){
while (std::getline(stream_in, line)) {
std::istringstream line_stream(line);
double x, y;
line_stream >> x >> y;
x_data.push_back(x);
y_data.push_back(y);
}
}
else{
std::cout << "Error Opening File" << std::endl;
return(EXIT_FAILURE);
}
// dinput data up to 1,000,000 points
long number_of_elements = x_data.size();
// centroids are initalized to first k points of array
// in order to chose 'randomly' we shuffle the array
// input array after we initilize the devize point array
// and before we initilize the centroid array
Point device_data(number_of_elements, x_data, y_data);
std::srand(std::time(0));
random_shuffle(x_data.begin(),x_data.end());
random_shuffle(y_data.begin(),y_data.end());
Point device_centroids(k, x_data, y_data);
int threads = 1024;
int blocks = (number_of_elements + threads - 1) / threads;
std::cout << "\nProcessing " << number_of_elements << " points\n" << std::endl;
int kmeans_shared_memory = 3 * threads * sizeof(double);
int centroid_reduction_memory = 2 * k * blocks * sizeof(double);
Point device_sum(k * blocks);
int * device_count;
ErrorCheck(cudaMalloc(&device_count, k * blocks * sizeof(int)), "Allocate size for device_count\n");
ErrorCheck(cudaMemset(device_count, 0, k * blocks * sizeof(int)),"Set device_count to '0' \n");
// cuda api time start
cudaEvent_t start,stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start,0);
// start iteration loop, assigning and updating centroid on each iteration
for (size_t iteration = 0; iteration < number_of_iterations; ++iteration) {
Assignment<<<blocks, threads, kmeans_shared_memory>>>(device_data.x,device_data.y, device_data.dataSize, device_centroids.x, device_centroids.y, device_sum.x, device_sum.y, k,
device_count);
cudaDeviceSynchronize();
centroid_recompute<<<1, k * blocks, centroid_reduction_memory>>>(device_centroids.x, device_centroids.y, device_sum.x, device_sum.y, k, device_count);
cudaDeviceSynchronize();
}
// cuda api time stop
cudaEventRecord(stop,0);
cudaEventSynchronize(stop);
float elapsedTime;
cudaEventElapsedTime(&elapsedTime,start,stop);
cudaEventDestroy(start);
cudaEventDestroy(stop);
std::string unit = "";
unit = (elapsedTime > 999) ? "seconds" : "milliseconds";
elapsedTime = (elapsedTime > 999 ) ? elapsedTime/1000 : elapsedTime;
std::cout << "Elapsed time of kernal calls: " << elapsedTime << " " << unit << "\n" << std::endl;
ErrorCheck(cudaFree(device_count),"Freeing Device Memory");
std::vector<double> centroid_x(k, 0);
std::vector<double> centroid_y(k, 0);
ErrorCheck(cudaMemcpy(centroid_x.data(), device_centroids.x, device_centroids.num_of_bytes, cudaMemcpyDeviceToHost), "Moving Array back to host\n");
ErrorCheck(cudaMemcpy(centroid_y.data(), device_centroids.y, device_centroids.num_of_bytes, cudaMemcpyDeviceToHost), "Moving Array back to host\n");
std::cout << "centroids:" << std::endl;
for (size_t cluster = 0; cluster < k; ++cluster) {
std::cout << centroid_x[cluster] << " " << centroid_y[cluster] << std::endl;
}
std::cout << "\n" << std::endl;
return(EXIT_SUCCESS);
}
|
18,762 | //Save data to array
// bool Cstmd1Sim::save_data_to_array(thrust::device_vector<float> &v, int** result, int T, int N){
//
// result = NULL;
//
// try {
//
// result = new int*[N];
// for(int i = 0; i < T; i++) {
// result[i] = new int[T];
// }
//
// } catch (std::bad_alloc& ba) {
// return false;
// }
//
// for (int j = 0; j < T; ++j) {
// for (int i = 0; i < N; ++i) {
// result[i][j] = v[i + j * N];
// }
// }
//
// return true;
// }
//Load Electrodes from files
// bool Cstmd1Sim::load_electrodes_from_file(const char* path) {
//
// int m_dim, n_dim;
// int ** matrix_2D;
//
// if(!load_matrix_from_file(path, matrix_2D, n_dim, m_dim)) {
// std::cerr << "Could not load electrode file" << std::endl;
// }
// if(n_dim != 1) {
// std::cerr << "Electrodes should be an nx1 array: (" << m_dim << "x" <<n_dim << " )" << std::endl;
// return false;
// }
//
// // Set N to the dimension
// nElectrodes = m_dim;
// bool success = load_electrodes(matrix_2D, nElectrodes);
//
// for(int i = 0; i < m_dim; ++i) {
// delete [] matrix_2D[i];
// }
//
// return success;
// }
//Load synapses from file
// bool Cstmd1Sim::load_synapses_from_file(const char * path) {
//
// int m_dim, n_dim;
// int ** matrix_2D;
//
// if(!load_matrix_from_file(path, matrix_2D, n_dim, m_dim)) {
// std::cerr << "Could not load synpase file" << std::endl;
// return false;
// } else if(n_dim != 2) {
// std::cerr << "Error cannot synapse not given in pairs found dimension: (" << m_dim << "x" <<n_dim << " )" << std::endl;
// return false;
// }
//
// nSynapses = m_dim;
// bool status = load_synapses(matrix_2D, nSynapses);
//
// for(int i = 0; i < m_dim; ++i) {
// delete [] matrix_2D[i];
// }
//
// return status;
// }
//Load estmd current from file
// bool Cstmd1Sim::load_estmd_currents_from_file(const char * path) {
// float ** data;
// int length, width;
// bool status = load_matrix_from_file(path,data,width,length);
// return load_estmd_currents(data,length);
// }
//Load morphology from file
// bool Cstmd1Sim::load_morphology_from_file(const char* path) {
//
// int m_dim, n_dim;
// int ** matrix_2D;
//
// if(!load_matrix_from_file(path, matrix_2D, n_dim, m_dim)) {
// std::cerr << "Could not load morphology file" << std::endl;
// }
// if(m_dim != n_dim) {
// std::cerr << "Error cannot use non-square morphology, got dimensions: (" << m_dim << "x" <<n_dim << " )" << std::endl;
// return false;
// }
//
// // Set N to the dimension
// nCompartments = m_dim;
//
// bool success = load_morphology(matrix_2D,nCompartments);
//
// for(int i = 0; i < m_dim; ++i) {
// delete [] matrix_2D[i];
// }
//
// return success;
// }
//Consume estmd current
// bool Cstmd1Sim::add_estmd_current_to_d_I(int currentTimeStep) {
// // Search for a estmd entry corresponding to the current time index
// for(int i = 0; i < h_estmd_input_time.size(); ++i) {
// if(h_estmd_input_time[i] == currentTimeStep) {
// if (CUBLAS_STATUS_SUCCESS != cublasSaxpy(handle,
// nCompartments,
// &one,
// thrust::raw_pointer_cast(&d_estmd_input_current[i*nCompartments]),
// 1,
// thrust::raw_pointer_cast(&d_I[0]),
// 1)) {
// std::cerr << "Base Current: Kernel execution error." << std::endl;
// return false;
// }
//
// return true;
// }
// // No estmd current found for this time
// if(h_estmd_input_time[i] > currentTimeStep) {
// return true;
// }
// }
//
// return true;
// }
//
// bool Cstmd1Sim::get_voltages(float** &voltages, int compartments[], int numberToGet, int t_min_sec, int t_max_sec) {
//
// int t_min_step = (int) ((float) t_min_sec / (float) dt);
// int t_max_step = (int) ((float) t_max_sec / (float) dt);
//
// if(t_min_step < 0) {
// std::cerr << "t_min too small" <<std::endl;
// return false;
// }
// if(t_max_step > T) {
// std::cerr << "t_max too big" <<std::endl;
// return false;
// }
// if(numberToGet > nCompartments) {
// std::cerr << "numberToGet too big" <<std::endl;
// return false;
// }
//
// try {
// voltages = new float*[numberToGet];
// for(int i = 0; i < numberToGet; ++i) {
// voltages[i] = new float[t_max_step - t_min_step];
// }
// } catch (std::bad_alloc &ba) {
// std::cerr << "Could not allocate space on the heap for voltage retrieval" << std::endl;
// return false;
// }
// std::cout << "Starting retrieval" << std::endl;
// for(int t = t_min_step; t < t_max_step; ++t) {
// for(int c = 0; c < numberToGet; ++c) {
// voltages[c][t] = d_v[t * nCompartments + compartments[c]];
// }
// }
// std::cout << "Finish retreival" << std::endl;
//
// getArray(voltages, d_v, t_min_step, t_max_step, compartments, numberToGet);
// return true;
//
// } |
18,763 | #include "includes.h"
__global__ void kApplyTanh(float* mat, float* target, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
float mat_i, exp2x;
for (unsigned int i = idx; i < len; i += numThreads) {
mat_i = mat[i];
exp2x = __expf(2 * mat_i);
target[i] = 1 - 2 / (exp2x + 1);
}
} |
18,764 | #include "includes.h"
__global__ void sway_and_flip_weights_kernel(const float *src_weight_gpu, float *weight_deform_gpu, int nweights, int n, int kernel_size, int angle, int reverse)
{
const int index = blockIdx.x*blockDim.x + threadIdx.x;
const int kernel_area = kernel_size * kernel_size;
const int i = index * kernel_area;
const int stage_step = (nweights / kernel_area) / 4; // 4 stages
const int stage_id = index / stage_step;
// nweights = (c / groups) * n * size * size;
// kernel_area = size*size
if (i < nweights)
{
if (stage_id == 0) {
// simple copy
for (int x = 0; x < kernel_size; ++x) {
for (int y = 0; y < kernel_size; ++y) {
weight_deform_gpu[x + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i];
}
}
}
else if (stage_id == 1 || stage_id == 2)
{
// rotate left or right
if (stage_id == 2) angle = -angle;
if (reverse) angle = -angle;
const float cos_a = cosf(angle * 3.14159265 / 180);
const float sin_a = sinf(angle * 3.14159265 / 180);
const int x_c = kernel_size / 2;
const int y_c = kernel_size / 2;
float dropout_sum = 0;
for (int y = 0; y < kernel_size; ++y) {
for (int x = 0; x < kernel_size; ++x) {
// Xsource = x*cos(alpha) + y*sin(alpha)
// Ysource = -x*sin(alpha) + y*cos(alpha)
float x_s = x_c + (x - x_c)*cos_a + (y - y_c)*sin_a;
float y_s = y_c - (x - x_c)*sin_a + (y - y_c)*cos_a;
int x_0 = floor(x_s); // round down
int x_1 = ceil(x_s); // round up
if (x_0 == x_1) x_1 = x_0 + 1;
int y_0 = floor(y_s);
int y_1 = ceil(y_s);
if (y_0 == y_1) y_1 = y_0 + 1;
float c_x_0 = x_1 - x_s;
float c_x_1 = x_s - x_0;
float c_y_0 = y_1 - y_s;
float c_y_1 = y_s - y_0;
float val = 0;
if (x_0 >= 0 && x_0 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_0 + y_0*kernel_size + i] * c_x_0 * c_y_0;
else dropout_sum += c_x_0 * c_y_0;
if (x_1 >= 0 && x_1 < kernel_size && y_0 >= 0 && y_0 < kernel_size) val += src_weight_gpu[x_1 + y_0*kernel_size + i] * c_x_1 * c_y_0;
else dropout_sum += c_x_1 * c_y_0;
if (x_0 >= 0 && x_0 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_0 + y_1*kernel_size + i] * c_x_0 * c_y_1;
else dropout_sum += c_x_0 * c_y_1;
if (x_1 >= 0 && x_1 < kernel_size && y_1 >= 0 && y_1 < kernel_size) val += src_weight_gpu[x_1 + y_1*kernel_size + i] * c_x_1 * c_y_1;
else dropout_sum += c_x_1 * c_y_1;
weight_deform_gpu[x + y*kernel_size + i] = val;
}
}
// compensate for dropped items
const float coef = (kernel_size*kernel_size) / (kernel_size*kernel_size - dropout_sum);
for (int y = 0; y < kernel_size; ++y) {
for (int x = 0; x < kernel_size; ++x) {
weight_deform_gpu[x + y*kernel_size + i] *= coef;
}
}
}
else if (stage_id == 3)
{
// flip
for (int y = 0; y < kernel_size; ++y) {
for (int x = 0; x < kernel_size; ++x) {
weight_deform_gpu[(kernel_size - x - 1) + y*kernel_size + i] = src_weight_gpu[x + y*kernel_size + i];
}
}
}
}
} |
18,765 | #include <stdio.h>
#include <cuda.h>
__global__ void gpu_reduce(int *c, int size)
{
/*Identificaciones necesarios*/
int IDX_Thread = threadIdx.x;
int IDY_Thread = threadIdx.y;
int IDX_block = blockIdx.x;
int IDY_block = blockIdx.y;
int shapeGrid_X = gridDim.x;
int threads_per_block = blockDim.x * blockDim.y;
int position = threads_per_block * ((IDY_block * shapeGrid_X)+IDX_block)+((IDY_Thread*blockDim.x)+IDX_Thread);
if(position<size){
if(size%2 != 0)
{
if(c[position]<c[size-1])
{
c[position]=c[size-1];
}
}else{
if(c[position]<c[position+size/2])
{
c[position]=c[position+size/2];
}
}
}
}
int reduce(int *c, int *v, int size,dim3 bd,dim3 gd)
{
int t=size;
while(t!=1){
gpu_reduce<<<gd,bd>>>(c,t);
cudaDeviceSynchronize();
if(size%2==0){
t=t/2;
}else{
size -= 1;
}
}
cudaMemcpy(v,c,sizeof(int) * size,cudaMemcpyDeviceToHost);
return v[0];
}
int main()
{
int *v, *vd, size, i;
size = 6;
if ((v=(int *) calloc(size, sizeof(int)))==NULL){
printf("error\n");
exit (-1);
}
cudaMalloc( (void**) &vd, sizeof(int) * (int) size);
int tam = (int) ceil( ((float)(size))/size);
dim3 bd(128,1);
dim3 gd(tam,1);
for(i=0;i<size;i++){
v[i]=size-i;
}
v[5]=50;
cudaMemcpy(vd,v,sizeof(int) * size,cudaMemcpyHostToDevice);
int max = reduce(vd,v, size,bd,gd);//despues de esta funcion hay que copiar el valor del vector del host en el vector device porque se descoloca
cudaMemcpy(vd,v,sizeof(int) * size,cudaMemcpyHostToDevice);
printf("%d\n",max);
cudaFree(vd);
free(v);
cudaDeviceReset();
}
|
18,766 | #include <thrust/device_vector.h>
#include <thrust/sequence.h>
#include <thrust/reduce.h>
#include <thrust/count.h>
#include <thrust/functional.h>
#include <thrust/execution_policy.h>
#include <thrust/scan.h>
#include <iostream>
using namespace std;
#define N 10
int main()
{
thrust::device_vector<int> D(N);
thrust::sequence(D.begin(), D.end());
int nines = thrust::count(D.begin(), D.end(), 9);
int sum1, sum2, sum3;
sum1 = thrust::reduce(D.begin(), D.end(), (int)0, thrust::plus<int>());
sum2 = thrust::reduce(D.begin(), D.end(), (int)0); // same
sum3 = thrust::reduce(D.begin(), D.end()); // same
cout << nines << " " << sum1 << " " << sum2 << " " << sum3 << endl;
int data[N] = {1,2,3,1,2,3,1,2,3,1}; // can use reduce for normal array!
int sum4 = thrust::reduce(data, data + N);
int sum5 = thrust::reduce(thrust::host, data, data + N, -1, thrust::maximum<int>());
cout << sum4 << " " << sum5 << endl;
int hostarr[N] = {1,2,3,4,5,1,2,3,4,5};
thrust::inclusive_scan(hostarr, hostarr + N, hostarr);
thrust::inclusive_scan(D.begin(), D.end(), D.begin());
thrust::copy(D.begin(), D.end(), ostream_iterator<int>(cout, " "));
cout << endl;
return 0;
}
|
18,767 | #include <stdio.h>
#include <sys/time.h>
#include <iostream>
#include <fstream>
using namespace std;
#define ALPHA 19e-5
#define DELTA_T 120
#define ROUNDS 3*60*60/DELTA_T
#define DISTANCE 0.1
/**
* O argumento deve ser double
*/
#define GET_TIME(now) { \
struct timespec time; \
clock_gettime(CLOCK_MONOTONIC_RAW, &time); \
now = time.tv_sec + time.tv_nsec/1000000000.0; \
}
/**
* Para checar erros em chamadas Cuda
*/
#define CUDA_SAFE_CALL(call) { \
cudaError_t err = call; \
if(err != cudaSuccess) { \
fprintf(stderr,"Erro no arquivo '%s', linha %i: %s.\n",__FILE__, __LINE__,cudaGetErrorString(err)); \
exit(EXIT_FAILURE); } \
}
void setupMatrix(double *A, int n){
for (int i=0; i<n; i++){
A[i] = 20;
A[n*n-i-1]=30;
}
double t = 20, a = 10.0/(n-1);
for (int i=1; i<n-1; i++){
t+=a;
A[i*n] = t;
for (int j=1; j<n-1; j++){
A[i*n+j]=20;
}
A[i*n + n - 1] = t;
}
}
__global__ void updateHeat(double *last, double *next , int n, int deltaT) {
int i = blockIdx.x * blockDim.x + threadIdx.x;
int j = blockIdx.y * blockDim.y + threadIdx.y;
int pos = i*n + j;
if (i==0 || i==n-1 || j==0 || j==n-1){
next[pos] = last[pos];
} else if (i < n && j < n){
next[pos] = last[pos] +
(ALPHA*deltaT/(DISTANCE*DISTANCE))*(last[pos-1]+last[pos+1]+last[pos-n]+last[pos+n]-4*last[pos]);
}
}
void print(double *A, int n){
cout << "#############################################################" << endl;
for (int i=0; i<n; i++){
for (int j=0; j<n; j++){
cout << A[i*n+j] << " ";
}
cout << endl;
}
cout << "#############################################################" << endl;
}
void playRounds(double **AdevicePointer, int n, int blockSize, int rounds, int deltaT) {
double *Atemp, *aux, *Adevice = AdevicePointer[0], *A;
size_t matBytes = n*n*sizeof(double);
A = (double *) malloc(matBytes);
CUDA_SAFE_CALL(cudaMalloc((void**) &Atemp, matBytes));
int nBlocks = (n + blockSize -1) / blockSize;
dim3 gBlocks(nBlocks, nBlocks);
dim3 nThreads(blockSize,blockSize);
for(int i=0; i<rounds; i++){
updateHeat <<< gBlocks, nThreads >>>(Adevice, Atemp, n, deltaT);
aux = Adevice;
Adevice = Atemp;
Atemp = aux;
}
CUDA_SAFE_CALL(cudaFree(Atemp));
AdevicePointer[0] =Adevice;
}
void playRoundsSeq(double **APointer, int n, int rounds, int deltaT) {
double *A = APointer[0], *Temp, *aux;
size_t matBytes = n*n*sizeof(double);
Temp = (double *) malloc(matBytes);
for(int r=0; r<rounds; r++){
for (int i = 0; i < n; ++i) {
Temp[i*n] = A[i*n];
for (int j = 1; j < n-1; ++j) {
int pos = i*n + j;
if (i==0 || i==n-1){
Temp[pos] = A[pos];
} else if (i < n && j < n){
Temp[pos] = A[pos] +
(ALPHA*deltaT/(DISTANCE*DISTANCE))*(A[pos-1]+A[pos+1]+A[pos-n]+A[pos+n]-4*A[pos]);
}
}
Temp[(i+1)*n-1] = A[(i+1)*n-1];
}
aux = A;
A = Temp;
Temp = aux;
}
free(Temp);
APointer[0] = A;
}
void printResults(int n, double timeCpuGpu, double timeRunPar, double timeGpuCpu){
cout << n << ";" << timeCpuGpu << ";" << timeRunPar << ";" << timeGpuCpu << endl;
}
int main(int argc, char** argv) {
int n=0, blockSize;
double *A, *Adevice;
double begin, end, timeCpuGpu = 0, timeRun = 0, timeGpuCpu = 0;
if(argc < 4) {
cerr << "Digite: "<< argv[0] <<" <Dimensão da matriz> <Tempo total> <Delta T> [Dimensão do bloco]" << endl;
exit(EXIT_FAILURE);
}
n = atol(argv[1]);
int deltaT = atol(argv[3]);
int rounds = atol(argv[2])/deltaT;
size_t matBytes = n*n*sizeof(double);
A = (double *) malloc(matBytes);
if ( A == NULL ) {
cerr << "Memoria insuficiente" << endl;
exit(EXIT_FAILURE);
}
setupMatrix(A, n);
if (argc > 4){
blockSize = atol(argv[4]);
GET_TIME(begin);
CUDA_SAFE_CALL(cudaMalloc((void**) &Adevice, matBytes));
CUDA_SAFE_CALL(cudaMemcpy(Adevice, A, matBytes, cudaMemcpyHostToDevice));
GET_TIME(end);
timeCpuGpu = end-begin;
GET_TIME(begin);
playRounds(&Adevice, n, blockSize, rounds, deltaT);
GET_TIME(end);
timeRun = end-begin;
GET_TIME(begin);
CUDA_SAFE_CALL(cudaMemcpy(A, Adevice, matBytes, cudaMemcpyDeviceToHost));
GET_TIME(end);
timeGpuCpu = end-begin;
// print(A, n);
CUDA_SAFE_CALL(cudaFree(Adevice));
} else {
GET_TIME(begin);
playRoundsSeq(&A, n, rounds, deltaT);
GET_TIME(end);
timeRun = end-begin;
}
printResults(n, timeCpuGpu, timeRun, timeGpuCpu);
free(A);
CUDA_SAFE_CALL(cudaDeviceReset());
exit(EXIT_SUCCESS);
}
|
18,768 | #include "includes.h"
__global__ void getPredicate_kernel(unsigned int * d_inVal, unsigned int * d_predVal, unsigned int numElems, unsigned int bitMask)
{
unsigned int gIdx = blockIdx.x * blockDim.x + threadIdx.x;
if (gIdx < numElems)
{
// if bitmask matches inputvale then assign 1 to the position otherwise set to 0
// we'll need to run an inclusive scan later to get the position
d_predVal[gIdx] = ((d_inVal[gIdx] & bitMask) == bitMask) ? 1 : 0;
//d_npredVal[gIdx] = ((d_inVal[gIdx] & bitMask) == bitMask) ? 0 : 1;
}
} |
18,769 | #include <iostream>
#include <stdlib.h> /* srand, rand */
#include <time.h> /* time */
#include <math.h>
using namespace std;
//4000x4000 8
#define row 50
#define column 65
#define THREADS_PER_BLOCK 1024//64//1024//8
// Funcion para generar numeros randoms en mi matrix:
void randomsInt(int **& matrix){
for(int i=0;i<row;++i){
for(int j=0;j<column;++j){
matrix[i][j] = 1; //rand()% 2 + 1;;
}
}
}
void imprimir(int **&M, int rows, int cols){
for(int i=0;i<rows;i++){
for(int j=0;j<cols;j++){
cout<<M[i][j]<<" ";
}
cout<<endl;
}
cout<<endl;
}
void resize(int **&M,int rows, int cols){
M = (int **) malloc(rows * sizeof(int*)) ;
for(int i = 0; i<rows; i++) {
M[i] = (int *) malloc(cols * sizeof(int));
}
}
void resize_matrix(int**& host, int rows, int cols ){
int size = rows* cols * sizeof(int*);
host = (int **)malloc(rows*sizeof(int*));
host[0]=(int *)malloc(size);
for (int i=1; i<rows;++i){
host[i]=host[i-1]+cols;
}
}
// Funcion imprimir:
void print(int ** a){
for(int i=0;i<row;++i){
for(int j=0;j<column;++j){
cout<<a[i][j]<<'\t';
}
cout<<endl;
}
cout<<endl;
}
// =====================================================================
void createMatrixCUDA(int**& device, int **& aux, int rows, int cols){
int size = rows* cols* sizeof(int*);
aux =(int **)malloc(rows*sizeof(int*));
cudaMalloc((void **)&aux[0],size);
cudaMalloc((void **)&device,rows*sizeof(int*));
for (int i=1; i<rows;++i){
aux[i]=aux[i-1]+cols;
}
cudaMemcpy(device, aux, rows*sizeof(int*), cudaMemcpyHostToDevice);
}
__global__ void sum(int **A, int **B, int **C){
int i = blockIdx.y * blockDim.y + threadIdx.y;
int j = blockIdx.x * blockDim.x + threadIdx.x;
if(i<row && j<column){
C[i][j] = A[i][j] + B[i][j];
}
}
void suma_cuda(int **a, int **b, int **c, int rows, int cols){
int **d_a, **d_b, **d_c;
int **a_aux, **b_aux, **c_aux;
int size = row* column * sizeof(int*);
createMatrixCUDA(d_a,a_aux,row,column);
createMatrixCUDA(d_b,b_aux,row,column);
createMatrixCUDA(d_c,c_aux,row,column);
cudaMemcpy(a_aux[0], a[0], size, cudaMemcpyHostToDevice);
cudaMemcpy(b_aux[0], b[0], size, cudaMemcpyHostToDevice);
dim3 threadPerBlock(THREADS_PER_BLOCK, THREADS_PER_BLOCK);
dim3 blockPerGrid((row+threadPerBlock.x-1)/threadPerBlock.x, (column+threadPerBlock.y-1)/threadPerBlock.y);
sum<<<blockPerGrid,threadPerBlock>>>(d_a,d_b,d_c);
//sum<<<(rows*cols+threadsPB-1)/threadsPB,threadsPB>>>(d_a, d_b, d_c);//run
cudaMemcpy(c[0],c_aux[0], size, cudaMemcpyDeviceToHost);
cudaFree(d_a);cudaFree(d_c);
cudaFree(a_aux); cudaFree(b_aux); cudaFree(c_aux);
//cudaFree(a_aux[0]);cudaFree(c_aux[0]);
}
// =====================================================================
int main(){
int rows=row;
int cols=column;
int **a, **b, **c;
resize_matrix(a,rows,cols);
resize_matrix(b,rows,cols);
resize_matrix(c,rows,cols);
randomsInt(a);
randomsInt(b);
//imagebn a, imagen b, imagen c (fx)
suma_cuda(a,b,c,rows,cols);
imprimir(a,rows,cols);
imprimir(b,rows,cols);
imprimir(c,rows,cols);
free(a); free(b); free(c);
return 0;
}
|
18,770 | #include "cudamat_kernels.cuh"
#include "float.h"
/* ------------------------- Random number generation ------------------------- */
__global__ void cudamat_kSeedRandom(unsigned int* rndMults, unsigned long long* rndWords, unsigned int seed) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
// The initial x is the seed and the initial carry is 1
unsigned long long rndWord = ((unsigned long long)seed << 32) + 1;
const unsigned int rndMult = rndMults[idx];
/*
* Run the chain for a few steps so that all the streams have a chance
* to differentiate. They start out generating similar random numbers
* because all the multipliers are similar.
*/
for(unsigned int i = 0; i < NUM_RND_BURNIN; i++) {
rndWord = rndMult * LOW_BITS(rndWord) + HIGH_BITS(rndWord);
}
rndWords[idx] = rndWord;
}
__global__ void cudamat_kRandomUniform(unsigned int* rndMults, unsigned long long* rndWords, float* gData, unsigned int numElements) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
unsigned long long rndWord = rndWords[idx];
const unsigned int rndMult = rndMults[idx];
for(unsigned int i = idx; i < numElements; i += NUM_RND_STREAMS) {
rndWord = rndMult * LOW_BITS(rndWord) + HIGH_BITS(rndWord);
gData[i] = (__uint2float_rn(LOW_BITS(rndWord)) + 1.0f) / 4294967296.0f;
}
rndWords[idx] = rndWord;
}
__global__ void cudamat_kRandomGaussian(unsigned int* rndMults, unsigned long long* rndWords, float* gData, unsigned int numElements) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
unsigned long long rndWord = rndWords[idx];
const unsigned int rndMult = rndMults[idx];
float rnd1, rnd2, R, T;
for(unsigned int i = idx; i < numElements; i += 2*NUM_RND_STREAMS) {
rndWord = rndMult * LOW_BITS(rndWord) + HIGH_BITS(rndWord);
rnd1 = (__uint2float_rn(LOW_BITS(rndWord)) + 1.0f) / 4294967296.0f;
rndWord = rndMult * LOW_BITS(rndWord) + HIGH_BITS(rndWord);
rnd2 = (__uint2float_rn(LOW_BITS(rndWord)) + 1.0f) / 4294967296.0f;
T = 2 * PI * rnd2;
R = sqrtf(-2 * __logf(rnd1));
gData[i] = R * __cosf(T);
if (i + NUM_RND_STREAMS < numElements)
gData[i + NUM_RND_STREAMS] = R * __sinf(T);
}
rndWords[idx] = rndWord;
}
/* ------------------------- Data copying ------------------------- */
/*
Copy row slice from source to target. There is a block for every 32x32 chunk being copied.
*/
__global__ void cudamat_kGetRowSlice(float* source, float* target, int start, int end, int width, int height) {
const int row = start + blockIdx.x * 32 + threadIdx.x;
const int start_col = blockIdx.y * 32;
const int end_col = (start_col + 32 < width) ? start_col + 32: width;
const int target_height = end - start;
if (row < end) {
for (int cur_col = start_col; cur_col < end_col; cur_col++)
target[cur_col * target_height + row - start] = source[cur_col * height + row];
}
}
__global__ void cudamat_kSetRowSlice(float* source, float* target, int start, int end, int width, int height) {
const int row = start + blockIdx.x * 32 + threadIdx.x;
const int start_col = blockIdx.y * 32;
const int end_col = (start_col + 32 < width) ? start_col + 32: width;
const int source_height = end - start;
if (row < end) {
for (int cur_col = start_col; cur_col < end_col; cur_col++)
target[cur_col * height + row] = source[cur_col * source_height + row - start];
//source[cur_col * height + row - start] = target[cur_col * target_height + row];
}
}
__global__ void cudamat_kTranspose(float *odata, float *idata, int width, int height) {
__shared__ float block[COPY_BLOCK_SIZE][COPY_BLOCK_SIZE+1];
// read the matrix tile into shared memory
unsigned int xIndex = blockIdx.x * COPY_BLOCK_SIZE + threadIdx.x;
unsigned int yIndex = blockIdx.y * COPY_BLOCK_SIZE + threadIdx.y;
if((xIndex < width) && (yIndex < height)) {
unsigned int index_in = yIndex * width + xIndex;
block[threadIdx.y][threadIdx.x] = idata[index_in];
}
__syncthreads();
// write the transposed matrix tile to global memory
xIndex = blockIdx.y * COPY_BLOCK_SIZE + threadIdx.x;
yIndex = blockIdx.x * COPY_BLOCK_SIZE + threadIdx.y;
if((xIndex < height) && (yIndex < width)) {
unsigned int index_out = yIndex * height + xIndex;
odata[index_out] = block[threadIdx.x][threadIdx.y];
}
}
/* ------------------------- Mathematical operations ------------------------- */
__global__ void cudamat_kLessThan(float* mat1, float* mat2, float* target, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < len; i += numThreads) {
target[i] = mat1[i] < mat2[i];
}
}
__global__ void cudamat_kLessThanScalar(float* mat, float val, float* target, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < len; i += numThreads) {
target[i] = mat[i] < val;
}
}
__global__ void cudamat_kGreaterThan(float* mat1, float* mat2, float* target, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < len; i += numThreads) {
target[i] = mat1[i] > mat2[i];
}
}
__global__ void cudamat_kGreaterThanScalar(float* mat, float val, float* target, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < len; i += numThreads) {
target[i] = mat[i] > val;
}
}
__global__ void cudamat_kMaxColumnwise(float* mat, float* target, unsigned int width, unsigned int height) {
__shared__ float max_vals[32];
float cur_max = -FLT_MAX;
float val = 0;
for (unsigned int i = threadIdx.x; i < height; i += 32) {
val = mat[blockIdx.x * height + i];
if (val > cur_max)
cur_max = val;
}
max_vals[threadIdx.x] = cur_max;
__syncthreads();
if (threadIdx.x == 0) {
cur_max = -FLT_MAX;
for (unsigned int i = 0; i < 32; i++)
if (max_vals[i] > cur_max)
cur_max = max_vals[i];
target[blockIdx.x] = cur_max;
}
}
__global__ void cudamat_kRowArgmax(float* mat,
float* target_max,
float* target_arg_max,
unsigned int width, unsigned int height) {
__shared__ float max_vals[32];
__shared__ float max_inds[32];
float cur_max = -FLT_MAX;
int ind_max = 0;
float val = 0;
for (unsigned int i = threadIdx.x; i < height; i += 32) {
val = mat[blockIdx.x * height + i];
if (val > cur_max){
cur_max = val;
ind_max = i;
}
}
max_vals[threadIdx.x] = cur_max;
max_inds[threadIdx.x] = ind_max;
__syncthreads();
if (threadIdx.x == 0) {
cur_max = -FLT_MAX;
for (unsigned int i = 0; i < 32; i++)
if (max_vals[i] > cur_max){
cur_max = max_vals[i];
ind_max = max_inds[i];
}
target_max[blockIdx.x] = cur_max;
target_arg_max[blockIdx.x * height + ind_max] = 1; // that's the way to go. Yes. Now we have the argmax.
}
}
__global__ void cudamat_kSign(float* mat, float* target, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < len; i += numThreads) {
target[i] = mat[i] ? copysignf(1., mat[i]) : 0.;
}
}
__global__ void cudamat_kApplySigmoid(float* mat, float* target, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < len; i += numThreads) {
target[i] = 1 / (1 + __expf(-mat[i]));
}
}
__global__ void cudamat_kApplyTanh(float* mat, float* target, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
float mat_i, exp2x;
for (unsigned int i = idx; i < len; i += numThreads) {
mat_i = mat[i];
exp2x = __expf(2 * mat_i);
target[i] = 1 - 2 / (exp2x + 1);
}
}
__global__ void cudamat_kApplyAbs(float* mat, float* target, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < len; i += numThreads) {
target[i] = mat[i] * ((mat[i] > 0) - (mat[i] < 0));
}
}
__global__ void cudamat_kApplyLog1PlusExp(float* mat, float* target, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
float mat_i;
for (unsigned int i = idx; i < len; i += numThreads) {
mat_i = mat[i];
if (mat_i > 0)
target[i] = (__logf(1 + __expf(-mat_i)) + mat_i);
else
target[i] = __logf(1 + __expf(mat_i));
}
}
__global__ void cudamat_kLog(float* mat, float* target, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < len; i += numThreads) {
target[i] = __logf(mat[i]);
}
}
__global__ void cudamat_kExp(float* mat, float* target, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < len; i += numThreads) {
target[i] = __expf(mat[i]);
}
}
__global__ void cudamat_kSqrt(float* mat, float* target, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < len; i += numThreads) {
target[i] = sqrt(mat[i]);
}
}
__global__ void cudamat_kPow(float* mat, float pow, float* target, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < len; i += numThreads) {
target[i] = powf(mat[i], pow);
}
}
__global__ void cudamat_kPowMatrix(float* mat, float* pow, float* target, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < len; i += numThreads) {
target[i] = powf(mat[i], pow[i]);
}
}
__global__ void cudamat_kReciprocal(float* mat, float* target, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < len; i += numThreads)
target[i] = 1. / mat[i];
}
__global__ void cudamat_kAddColVector(float* mat, float* vec, float* tgtMat, unsigned int width,
unsigned int height) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < width * height; i += numThreads) {
tgtMat[i] = mat[i] + vec[i % height];
}
}
__global__ void cudamat_kAddRowVector(float* mat, float* vec, float* tgtMat, unsigned int width, unsigned int height) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < width * height; i += numThreads) {
tgtMat[i] = mat[i] + vec[i / height];
}
}
__global__ void cudamat_kAddColMult(float* mat, float* vec, float* tgtMat, float mult,
unsigned int width, unsigned int height) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < width * height; i += numThreads) {
tgtMat[i] = mat[i] + mult * vec[i % height];
}
}
__global__ void cudamat_kMultByColVector(float* mat, float* vec, float* tgtMat, unsigned int width, unsigned int height) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < width * height; i += numThreads) {
tgtMat[i] = mat[i] * vec[i % height];
}
}
__global__ void cudamat_kMultByRowVector(float* mat, float* vec, float* tgtMat, unsigned int width, unsigned int height) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < width * height; i += numThreads) {
tgtMat[i] = mat[i] * vec[i / height];
}
}
__global__ void cudamat_kAdd(float* a, float* b, float* dest, unsigned int numEls) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < numEls; i += numThreads) {
dest[i] = a[i] + b[i];
}
}
__global__ void cudamat_kSubtract(float* a, float* b, float* dest, unsigned int numEls) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < numEls; i += numThreads) {
dest[i] = a[i] - b[i];
}
}
__global__ void cudamat_kDivide(float* a, float* b, float* dest, unsigned int numEls) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < numEls; i += numThreads) {
dest[i] = a[i] / b[i];
}
}
__global__ void cudamat_kMult(float* a, float* b, float* dest, unsigned int numEls) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < numEls; i += numThreads) {
dest[i] = a[i] * b[i];
}
}
__global__ void cudamat_kMultScalar(float* mat, float alpha, float* dest, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < len; i += numThreads) {
dest[i] = alpha * mat[i];
}
}
__global__ void cudamat_kAssignScalar(float* dest, float alpha, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < len; i += numThreads) {
dest[i] = alpha;
}
}
__global__ void cudamat_kDivideScalar(float* mat, float alpha, float* dest, unsigned int len) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < len; i += numThreads) {
dest[i] = mat[i] / alpha;
}
}
__global__ void cudamat_kAddScalar(float* a, float alpha, float* dest, unsigned int numEls) {
const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x;
const unsigned int numThreads = blockDim.x * gridDim.x;
for (unsigned int i = idx; i < numEls; i += numThreads) {
dest[i] = a[i] + alpha;
}
}
// __global__ void cudamat_kSelectRows(float* source, float* target, float* indices, int nRowIs, int nCols, int nSourceRows){
// __shared__ int sourceRowIndices[32];
// const int startTargetRowI = blockIdx.x * 32;
// const int tid = threadIdx.x;
// const int localNRowIs = min(32, nRowIs-startTargetRowI);
// // cooperatively load 32 row indices
// if (tid < localNRowIs){
// sourceRowIndices[tid] = int(indices[startTargetRowI + tid]);
// if (sourceRowIndices[tid]<0)
// sourceRowIndices[tid] += nSourceRows;
// if (sourceRowIndices[tid]<0 || sourceRowIndices[tid]>=nSourceRows)
// sourceRowIndices[tid] = -1;
// }
// __syncthreads();
// // copy 32 rows
// for (int i=0; i<localNRowIs; i++){
// const int targetRowI = startTargetRowI + i, sourceRowI = sourceRowIndices[i];
// for (int colI=tid; colI<nCols; colI+=32)
// target[targetRowI * nCols + colI] = sourceRowI==-1 ? (1.0/0.0 -1.0/0.0) : source[sourceRowI * nCols + colI];
// }
// }
__global__ void cudamat_kSetSelectedRows(float* target, float* source, float* indices, int nRowIs, int nCols, int nTargetRows){
__shared__ int targetRowIndices[32];
const int startSourceRowI = blockIdx.x * 32;
const int tid = threadIdx.x;
const int localNRowIs = min(32, nRowIs-startSourceRowI);
// cooperatively load 32 row indices
if (tid < localNRowIs){
targetRowIndices[tid] = int(indices[startSourceRowI + tid]);
if (targetRowIndices[tid]<0)
targetRowIndices[tid] += nTargetRows;
if (targetRowIndices[tid]<0 || targetRowIndices[tid]>=nTargetRows)
targetRowIndices[tid] = -1;
}
__syncthreads();
// copy 32 rows
for (int i=0; i<localNRowIs; i++){
const int sourceRowI = startSourceRowI + i, targetRowI = targetRowIndices[i];
for (int colI=tid; colI<nCols; colI+=32)
target[targetRowI * nCols + colI] = targetRowI==-1 ? (1.0/0.0 -1.0/0.0) : source[sourceRowI * nCols + colI];
}
}
__global__ void cudamat_kSelectRows(float* source, float* target, float* indices, int nRowIs, int nCols, int nSourceRows){
__shared__ int sourceRowIndices[32];
const int tid = threadIdx.x, bid = blockIdx.x*gridDim.y+blockIdx.y;
const int startTargetRowI = bid * 32;
if (startTargetRowI>=nRowIs) return; // unneeded block
const int localNRowIs = min(32, nRowIs-startTargetRowI);
// cooperatively load 32 row indices
if (tid < localNRowIs){
sourceRowIndices[tid] = int(indices[startTargetRowI + tid]);
if (sourceRowIndices[tid]<0)
sourceRowIndices[tid] += nSourceRows;
if (sourceRowIndices[tid]<0 || sourceRowIndices[tid]>=nSourceRows)
sourceRowIndices[tid] = -1;
}
__syncthreads();
// copy 32 rows
for (int i=0; i<localNRowIs; i++){
const int targetRowI = startTargetRowI + i, sourceRowI = sourceRowIndices[i];
for (int colI=tid; colI<nCols; colI+=32)
target[targetRowI * nCols + colI] = sourceRowI==-1 ? (1.0/0.0 -1.0/0.0) : source[sourceRowI * nCols + colI];
}
}
__global__ void cudamat_kClfPcOuterProduct(int maxNIndexPairs, float* GindexPairs, float* nIndexPairss, float* A, float* B, float* ret, int nCols, int nBlocks){
/*
a block per row. a thread per ret cell (except: at least 32 threads).
dynamically allocated shared mem: two words per index pair.
*/
const int tid = threadIdx.x, bid = blockIdx.x*gridDim.y+blockIdx.y;
if (bid>=nBlocks) return;
const int nIndexPairs = nIndexPairss[bid];
extern __shared__ int indexPairs[];
// cooperatively load index pairs
GindexPairs += bid * maxNIndexPairs*2;
const int nValuesToCopy = nIndexPairs * 2;
if (tid<32) for (int i=tid; i<nValuesToCopy; i+=32) indexPairs[i] = GindexPairs[i];
__syncthreads();
// from now on, everything is thread local
if (tid<nCols){
float cum=0;
for (int iPairI=0; iPairI<nIndexPairs; iPairI++){
const int aI = indexPairs[iPairI*2], bI = indexPairs[iPairI*2+1];
cum += A[nCols*aI + tid] * B[nCols*bI + tid];
}
ret[nCols*bid + tid] = cum;
}
}
__global__ void cudamat_kClfVsProduct(int nComponents, int vectorLength, int nothingIndex_scalars, float* inVectors, float* outVectors, float* globalScalars, float* inIndices, int nBlocks){
__shared__ int vectorIndices[32];
__shared__ float localScalars[32];
const int tid = threadIdx.x, bid = blockIdx.x*gridDim.y+blockIdx.y;
if (bid>=nBlocks) return;
inIndices += bid * 2 * nComponents;
float cum=0;
for (int componentI=0; componentI<nComponents; componentI++){
if ((componentI & 31) == 0){ // download the next 32 scalars & indices
if (tid<32 && componentI+tid<nComponents){
vectorIndices[tid] = inIndices[componentI+tid];
const int scalarIndex = inIndices[componentI+tid + nComponents];
localScalars[tid] = (scalarIndex==nothingIndex_scalars) ? 0 : globalScalars[scalarIndex];
}
}
__syncthreads(); // this should of course be in the conditional that just closed, but putting it there seems to make it not work.
if ((tid < vectorLength) && (localScalars[componentI & 31] != 0))
cum += localScalars[componentI & 31] * inVectors[vectorIndices[componentI & 31] * vectorLength + tid];
}
if (tid < vectorLength)
outVectors[bid * vectorLength + tid] = cum;
}
|
18,771 | #include "includes.h"
__global__ void cuConvertC3ToC4Kernel(const float3* src, size_t src_stride, float4* dst, size_t dst_stride, int width, int height)
{
const int x = blockIdx.x*blockDim.x + threadIdx.x;
const int y = blockIdx.y*blockDim.y + threadIdx.y;
int c_src = y*src_stride + x;
int c_dst = y*dst_stride + x;
if (x<width && y<height)
{
float3 val=src[c_src];
dst[c_dst] = make_float4(val.x, val.y, val.z, 1.0f);
}
} |
18,772 | #include<stdio.h>
#define SIZE 100
__global__
void max(int *a,int *b,int *c)
{
int i = blockIdx.x*blockDim.x + threadIdx.x;
int id;
switch(i)
{
case 0:
for(id = 0 ; id<SIZE/10 ; id++)
{
if(a[id] > c[i])
c[i] = a[id];
}
b[i] = c[i];
break;
case 1:
for(id = SIZE/10 ; id<(SIZE/10)+10 ; id++)
{
if(a[id] > c[i])
c[i] = a[id];
}
b[i] = c[i];
break;
case 2:
for(id = (SIZE/10)+10 ; id< (SIZE/10)+20 ; id++)
{
if(a[id] > c[i])
c[i] = a[id];
}
b[i] = c[i];
break;
case 3:
for(id = (SIZE/10)+20 ; id< (SIZE/10)+30 ; id++)
{
if(a[id] > c[i])
c[i] = a[id];
}
b[i] = c[i];
break;
case 4:
for(id = (SIZE/10)+30 ; id<(SIZE/10)+40 ; id++)
{
if(a[id] > c[i])
c[i] = a[id];
}
b[i] = c[i];
break;
case 5:
for(id = (SIZE/10)+40 ; id<(SIZE/10)+50 ; id++)
{
if(a[id] > c[i])
c[i] = a[id];
}
b[i] = c[i];
break;
case 6:
for(id = (SIZE/10)+50 ; id< (SIZE/10)+60 ; id++)
{
if(a[id] > c[i])
c[i] = a[id];
}
b[i] = c[i];
break;
case 7:
for(id =(SIZE/10)+60 ; id< (SIZE/10)+70 ; id++)
{
if(a[id] > c[i])
c[i] = a[id];
}
b[i] = c[i];
break;
case 8:
for(id =(SIZE/10)+70 ; id< (SIZE/10)+80 ; id++)
{
if(a[id] > c[i])
c[i] = a[id];
}
b[i] = c[i];
break;
case 9:
for(id =(SIZE/10)+80 ; id<(SIZE/10)+90 ; id++)
{
if(a[id] > c[i])
c[i] = a[id];
}
b[i] = c[i];
break;
}
}
__global__
void max2(int *a , int *b , int *c)
{
int i = blockIdx.x*blockDim.x + threadIdx.x;
int big=-9999,big2 = -9999;
int id;
if ( i==0 )
{
for(id =0 ; id<5 ; id++)
{
if(a[id] > big)
big = a[id];
}
*b = big;
}
if(i==1)
{
for(id =5 ; id<10 ; id++)
{
if(a[id] > big2)
big2 = a[id];
}
*c = big2;
}
}
int main()
{
int a[100],b=0,c=0,i,big[10],big2[10];
int *d_a,*d_b,*d_c,*d_d,*d_one,*d_two;
for(i=0;i<SIZE;i++)
{
a[i] = rand();
}
for(i=0;i<SIZE/10;i++)
{
big[i] = 0;
big2[i] = -9999;
}
printf("\n\tPrinting Array : ");
for(i=0;i<SIZE;i++)
printf("\n%d",a[i]);
cudaMalloc(&d_a,SIZE*sizeof(int));
cudaMemcpy(d_a,a,SIZE*sizeof(int),cudaMemcpyHostToDevice);
cudaMalloc(&d_b,SIZE*sizeof(int));
cudaMemcpy(d_b,big,SIZE*sizeof(int),cudaMemcpyHostToDevice);
cudaMalloc(&d_c,SIZE*sizeof(int));
cudaMemcpy(d_c,big2,SIZE*sizeof(int),cudaMemcpyHostToDevice);
max<<<1,10>>>(d_a,d_b,d_c);
cudaDeviceSynchronize();
cudaMemcpy(big,d_b,SIZE*sizeof(int),cudaMemcpyDeviceToHost);
printf("\n\n\tPrinting Max elements among array of 100 : ");
for(i=0;i<SIZE/10;i++)
printf("\n\t%d",big[i]);
cudaMalloc(&d_d,10*sizeof(int));
cudaMemcpy(d_d,big,10*sizeof(int),cudaMemcpyHostToDevice);
cudaMalloc((void**)&d_one,sizeof(int));
cudaMalloc((void**)&d_two,sizeof(int));
max2<<<1,2>>>(d_d,d_one,d_two);
cudaDeviceSynchronize();
cudaMemcpy(&b,d_one,sizeof(int),cudaMemcpyDeviceToHost);
cudaMemcpy(&c,d_two,sizeof(int),cudaMemcpyDeviceToHost);
if(b>c)
printf("\n\tMax = %d",b);
else
printf("\n\tMax = %d",c);
} |
18,773 | #include "thrust_all.cuh"
__global__
void device_add(int *a,int *b,int N)
{
auto index = blockIdx.x * blockDim.x + threadIdx.x;
auto stride = blockDim.x * gridDim.x;
for (auto i = index; i < N; i += stride)
b[i] = a[i] + b[i];
}
int main(void)
{
constexpr int N = 1<<10;
using vec_type = int;
thrust::device_vector<vec_type> a(N);
thrust::device_vector<vec_type> b(N);
thrust::host_vector<vec_type> c(N);
thrust::fill(a.begin(),a.end(),1);
thrust::fill(b.begin(),b.end(),2);
//auto add_vec = thrust::plus<vec_type>();
//thrust::transform(a.begin(),a.end(),b.begin(),b.begin(),add_vec);
int *a_ptr =thrust::raw_pointer_cast(&a[0]);
int *b_ptr =thrust::raw_pointer_cast(&b[0]);
int blockSize = 256;
int numBlocks = (N + blockSize - 1) / blockSize;
device_add<<<numBlocks,blockSize>>>(a_ptr,b_ptr,N);
thrust::copy(b.begin(),b.end(),c.begin());
for(auto i = c.begin(),last = c.end(); i!= last;++i){
std::cout << *i << '\n';
}
return 0;
}
|
18,774 | #include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <iostream>
using namespace std;
int main(){
cudaDeviceProp prop;
int counted;
cudaGetDeviceCount(&counted);
for(int i=0; i<counted; i++){
cudaGetDeviceProperties(&prop,i);
cout<<"---Some Information for the Device---"<<endl;
cout<<"Name : " << prop.name << endl;
cout<<"Compute capability : " << prop.major << "."<< prop.minor << endl;
cout<<"Clock Rate : " << prop.clockRate << endl;
}
} |
18,775 |
/* This is a automatically generated test. Do not modify */
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
__global__
void compute(float comp, int var_1,int var_2,int var_3,float var_4,float var_5,float var_6,float var_7,float var_8,float* var_9,float var_10,float var_11,float var_12,float var_13,float var_14,float var_15,float var_16,float var_17,float* var_18,float var_19,float var_20,float var_21,float var_22) {
if (comp <= powf(logf(var_4 / +1.0442E13f), +1.9354E-35f)) {
if (comp > (+1.1307E36f + var_5 - +1.7798E35f)) {
for (int i=0; i < var_1; ++i) {
float tmp_1 = var_6 - (-1.4880E-41f / (+0.0f - var_7));
comp = tmp_1 / var_8 + -1.9027E20f;
for (int i=0; i < var_2; ++i) {
comp = (var_10 - log10f((var_11 / var_12 / (var_13 * coshf(-1.6652E34f / atan2f(log10f(-1.8365E35f), -1.4798E-41f))))));
var_9[i] = var_14 + (var_15 / (var_16 + var_17));
comp += var_9[i] - (+1.3296E35f - (+1.1937E-41f - -1.2303E26f));
}
for (int i=0; i < var_3; ++i) {
var_18[i] = +1.8421E-26f;
comp = var_18[i] + var_19 / (-1.3408E19f - +1.2903E-41f - var_20);
comp += (-1.3598E-43f * var_21 * var_22);
}
}
}
}
printf("%.17g\n", comp);
}
float* initPointer(float v) {
float *ret = (float*) malloc(sizeof(float)*10);
for(int i=0; i < 10; ++i)
ret[i] = v;
return ret;
}
int main(int argc, char** argv) {
/* Program variables */
float tmp_1 = atof(argv[1]);
int tmp_2 = atoi(argv[2]);
int tmp_3 = atoi(argv[3]);
int tmp_4 = atoi(argv[4]);
float tmp_5 = atof(argv[5]);
float tmp_6 = atof(argv[6]);
float tmp_7 = atof(argv[7]);
float tmp_8 = atof(argv[8]);
float tmp_9 = atof(argv[9]);
float* tmp_10 = initPointer( atof(argv[10]) );
float tmp_11 = atof(argv[11]);
float tmp_12 = atof(argv[12]);
float tmp_13 = atof(argv[13]);
float tmp_14 = atof(argv[14]);
float tmp_15 = atof(argv[15]);
float tmp_16 = atof(argv[16]);
float tmp_17 = atof(argv[17]);
float tmp_18 = atof(argv[18]);
float* tmp_19 = initPointer( atof(argv[19]) );
float tmp_20 = atof(argv[20]);
float tmp_21 = atof(argv[21]);
float tmp_22 = atof(argv[22]);
float tmp_23 = atof(argv[23]);
compute<<<1,1>>>(tmp_1,tmp_2,tmp_3,tmp_4,tmp_5,tmp_6,tmp_7,tmp_8,tmp_9,tmp_10,tmp_11,tmp_12,tmp_13,tmp_14,tmp_15,tmp_16,tmp_17,tmp_18,tmp_19,tmp_20,tmp_21,tmp_22,tmp_23);
cudaDeviceSynchronize();
return 0;
}
|
18,776 | //============================================================================
// Name : TCcalcJacobiParallel.cpp
// Author : Niklas Bergh
//============================================================================
#include <iostream>
#include <fstream>
#include <sstream>
#include <unordered_map>
#include <vector>
#include <string> // std::stoi
#include <algorithm>
#include <assert.h> // cudaCheckReturn
#ifndef BLOCK_SIZE
#define BLOCK_SIZE 16
#endif
#ifndef MAX_ITERATIONS
#define MAX_ITERATIONS 50
#endif
/* This solver uses the Jacobi/Gauss Seidel method, see https://www3.nd.edu/~zxu2/acms40390F12/Lec-7.3.pdf. It
* converges for some systems, but not all
*/
__device__ int errorOnGPU;
__device__ inline int getValFromMatrix(int* matrix, int row, int col,int matSize,int pitchLength) {
if (row<matSize && col<matSize) {return matrix[row*pitchLength + col];}
return 0;
}
__device__ inline int getValFromVector(int* vector, int row, int matSize) {
if (row<matSize) {return vector[row];}
return 0;
}
__global__ void jacobiIterateKernel(int* cOnGPU,int* bOnGPU, int* xOnGPU, int* deltaXonGPU, int matSize, int pitchLength) {
__shared__ int cShared[BLOCK_SIZE][BLOCK_SIZE+1]; // Make the columns BLOCK_SIZE + 1, so that there is less chance of a shared memory bank conflict
__shared__ int xShared[BLOCK_SIZE];
int myRow = blockIdx.x * blockDim.x + threadIdx.x;
int myRowInBlock = threadIdx.x, myColInBlock = threadIdx.y;
int rowSum=getValFromVector(bOnGPU,myRow,matSize);
for (int m = 0; m < (matSize + BLOCK_SIZE - 1) / BLOCK_SIZE; m++) {
cShared[myRowInBlock][myColInBlock] = getValFromMatrix(cOnGPU,myRow,m*BLOCK_SIZE+myColInBlock,matSize,pitchLength);
if (myColInBlock==0) {xShared[myRowInBlock] = getValFromVector(xOnGPU,m*BLOCK_SIZE+myRowInBlock,matSize);}
__syncthreads(); // Sync threads to make sure all fields have been written by all threads in the block to cShared and xShared
if (myColInBlock==0) {
for (int k=0;k<BLOCK_SIZE;k++) {
// Only the Jacobi (not Gauss Seidel) iteration works in parallell:
if (m*BLOCK_SIZE+k==myRow) {continue;}
rowSum += cShared[myRowInBlock][k] * xShared[k];
}
}
__syncthreads(); // Sync here so that no threads start changing the shared arrays (in the next iteration of m) before rowSum has been updated
}
if (myColInBlock==0 && myRow<matSize) {
deltaXonGPU[myRow] = abs(rowSum - xOnGPU[myRow]);
xOnGPU[myRow] = rowSum; // Update x
}
}
__global__ void calculateErrorKernel(int* deltaXonGPU, int matSize) {
__shared__ int deltaXshared[BLOCK_SIZE];
__shared__ int sharedError;
int myIndexInBlock = threadIdx.x;
sharedError=0;
for (int m = 0; m < (matSize + BLOCK_SIZE - 1) / BLOCK_SIZE;m++) {
deltaXshared[myIndexInBlock] = getValFromVector(deltaXonGPU,m*BLOCK_SIZE+myIndexInBlock,matSize);
__syncthreads();
if (myIndexInBlock==0) {
for (int k=0;k<BLOCK_SIZE;k++) {
sharedError+=deltaXshared[k];
}
}
__syncthreads();
}
if (myIndexInBlock==0) {
errorOnGPU = sharedError;
}
}
static inline void cudaCheckReturn(cudaError_t result) {
if (result != cudaSuccess) {
std::cerr <<"CUDA Runtime Error: " << cudaGetErrorString(result) << std::endl;
assert(result == cudaSuccess);
}
}
static int jacobiIterate(int* cOnGPU,int* bOnGPU, int* xOnGPU, int* deltaXonGPU, int matSize, int pitchLength) {
int nrOfBlocks = (matSize + BLOCK_SIZE -1) / BLOCK_SIZE;
dim3 threadSize(BLOCK_SIZE,BLOCK_SIZE);
jacobiIterateKernel<<<nrOfBlocks,threadSize>>>(cOnGPU,bOnGPU,xOnGPU,deltaXonGPU,matSize,pitchLength);
calculateErrorKernel<<<1,BLOCK_SIZE>>>(deltaXonGPU,matSize); // Alternatively deltaXonGPU can be copied to an array defined here, and iterate through it in CPU to get the error
int error=0;
// This call synchronizes the device with the host, so there is no application code running on the device past this point
cudaCheckReturn(cudaMemcpyFromSymbol(&error,errorOnGPU,sizeof(int),0,cudaMemcpyDeviceToHost));
return error;
}
int main(int argc, char** argv) {
if (argc<2) {std::cerr << "No equation file provided in command line argument" << std::endl; return -1;}
// Check BLOCK_SIZE
int blockSizeIn = BLOCK_SIZE;
if (blockSizeIn<0 || blockSizeIn-BLOCK_SIZE!=0 || (blockSizeIn*blockSizeIn)%32!=0) {
std::cerr << "Block size must be > 0, an integer and its square must be a multiple of 32" << std::endl;
return -1;
}
// Check MAX_ITERATIONS
int maxIterations = MAX_ITERATIONS;
if (maxIterations<0 || maxIterations-MAX_ITERATIONS != 0) {
std::cerr << "Illegal format of MAX_ITERATIONS" << std::endl;
return -1;
}
// Start by reading the input file
std::unordered_map<std::string, int> variableMap;
std::vector<std::string> variableList;
std::ifstream inFile;
std::istringstream ss;
std::string line,token;
int** C,* b; // variable coefficients, equation constants
int* x; // variables, variables in the new iteration
int lineIndex=0,nrOfEquations=0,iters=0;
bool firstToken=true;
inFile.open(argv[1]);
if (inFile.is_open()) {
while (getline(inFile,line)) {
ss.str(line);
ss.clear();
ss >> token;
variableMap.insert({token,nrOfEquations});
variableList.push_back(token);
nrOfEquations++;
}
if (nrOfEquations==0) {std::cerr << "No equations in input file" << std::endl;return-1;}
// Allocate the matrices, zero initialized with ()
C = new int*[nrOfEquations];
C[0] = new int[nrOfEquations*nrOfEquations];
for (int i = 1; i < nrOfEquations;i++) {C[i] = &C[0][i*nrOfEquations];}
b = new int[nrOfEquations]();
x = new int[nrOfEquations]();
inFile.clear();
inFile.seekg(std::ios::beg); // Reset file pointer
while (getline(inFile,line)) {
C[lineIndex][lineIndex]=-1;
ss.str(line);
ss.clear();
while (ss >> token) {
if (firstToken) {firstToken=false;continue;}
if (isalpha(token[0])) {
C[lineIndex][variableMap.at(token)]++; // Add to coefficients
}
else if (isdigit(token[0])) {
b[lineIndex]+=std::stoi(token); // Assuming no int overflow here
}
}
if (C[lineIndex][lineIndex]!=-1) {
/* C[lineIndex][lineIndex] cannot be zero. It is ok for it to be -1 or greater than 0. In the case it is not -1 or 0
* then all the other coefficients and b[lineIndex] needs to be divided by that -coefficient. I assume that it never happens
* here though, and only allow C[lineIndex][lineIndex] to be -1
*/
std::cerr << "Error, coefficient for diagonal variable is not 1" << std::endl;
return -1;
}
firstToken=true;
lineIndex++;
}
}
else {
std::cerr << "Unable to open file" << std::endl;
return -1;
}
inFile.close();
// Allocate data on the GPU:
int* cOnGPU,* bOnGPU;
int* xOnGPU,* deltaXonGPU;
int rowSizeOnGPU = nrOfEquations * sizeof(int),pitchLength;
size_t cPitch;
cudaCheckReturn(cudaMallocPitch(&cOnGPU,&cPitch,rowSizeOnGPU,nrOfEquations));
cudaCheckReturn(cudaMalloc(&bOnGPU,rowSizeOnGPU));
cudaCheckReturn(cudaMalloc(&xOnGPU,rowSizeOnGPU));
cudaCheckReturn(cudaMalloc(&deltaXonGPU,rowSizeOnGPU));
pitchLength = cPitch/sizeof(int);
cudaCheckReturn(cudaMemcpy2D(cOnGPU,cPitch,C[0],rowSizeOnGPU,rowSizeOnGPU,nrOfEquations,cudaMemcpyHostToDevice));
cudaCheckReturn(cudaMemcpy(bOnGPU,b,rowSizeOnGPU,cudaMemcpyHostToDevice));
cudaCheckReturn(cudaMemset(xOnGPU,0,rowSizeOnGPU));
while (++iters<MAX_ITERATIONS && jacobiIterate(cOnGPU,bOnGPU,xOnGPU,deltaXonGPU,nrOfEquations,pitchLength) > 0); // Iterate until convergence
if (iters==MAX_ITERATIONS) {std::cerr << "Jacobi method did not converge" << std::endl;return -1;}
cudaCheckReturn(cudaMemcpy(x,xOnGPU,rowSizeOnGPU,cudaMemcpyDeviceToHost));
cudaCheckReturn(cudaFree(cOnGPU));
cudaCheckReturn(cudaFree(bOnGPU));
cudaCheckReturn(cudaFree(xOnGPU));
cudaCheckReturn(cudaFree(deltaXonGPU));
// Associate each variable string with its value:
std::vector<std::pair<std::string,int>> varPairs;
varPairs.reserve(nrOfEquations);
for (int i=0;i<nrOfEquations;i++) {
varPairs.push_back(std::make_pair(variableList[i],x[i]));
}
// Sort the strings:
sort( varPairs.begin(), varPairs.end());
// Print the result:
for (int i=0;i<nrOfEquations;i++) {
std::cout << varPairs[i].first << " = " << varPairs[i].second << std::endl;
}
delete[] C[0];
delete[] C;
delete[] b;
delete[] x;
}
|
18,777 | /////////////////////////
// convolution.cu //
// Andrew Krepps //
// Module 5 Assignment //
// 3/5/2018 //
/////////////////////////
#include <chrono>
#include <stdio.h>
#include <stdlib.h>
#define MAX_WEIGHTS 4096
///////////////////////////////////////////////////////////////////////////////
/// \brief perform convolution operation for a single output element
///
/// \param [in] inVec the input data vector
/// \param [in] inWeights the input weight vector
/// \param [out] outVec the output data vector
/// \param [in] numElements the number of input and output vector elements
/// \param [in] numWeights the number of weights
///////////////////////////////////////////////////////////////////////////////
__device__ void performConvolution(
const float* inVec,
const float* inWeights,
float* outVec,
const unsigned int numElements,
const unsigned int numWeights)
{
const unsigned int dataIdx = blockIdx.x*blockDim.x + threadIdx.x;
if (dataIdx < numElements) {
outVec[dataIdx] = 0.0f;
for (unsigned int wIdx = 0; wIdx < numWeights; ++wIdx) {
if (dataIdx + wIdx < numElements) {
outVec[dataIdx] += inVec[dataIdx+wIdx]*inWeights[wIdx];
}
}
}
}
///////////////////////////////////////////////////////////////////////////////
/// \brief perform convolution using global memory
///
/// \param [in] inVec the input data vector
/// \param [in] inWeights the input weight vector
/// \param [out] outVec the output data vector
/// \param [in] numElements the number of input and output vector elements
/// \param [in] numWeights the number of weights
///////////////////////////////////////////////////////////////////////////////
__global__ void convolutionGlobalMem(
const float* inVec,
const float* inWeights,
float* outVec,
const unsigned int numElements,
const unsigned int numWeights)
{
// we're just using global memory, so directly perform convolution
performConvolution(inVec, inWeights, outVec, numElements, numWeights);
}
///////////////////////////////////////////////////////////////////////////////
// \brief constant memory for storing convolution weights
///////////////////////////////////////////////////////////////////////////////
__constant__ float weightsConstantMem[MAX_WEIGHTS];
///////////////////////////////////////////////////////////////////////////////
/// \brief perform convolution using constant memory
///
/// This assumes that the weights have already been copied to constant memory
/// using the symbol weightsConstantMem.
///
/// \param [in] inVec the input data vector
/// \param [out] outVec the output data vector
/// \param [in] numElements the number of input and output vector elements
/// \param [in] numWeights the number of weights
///////////////////////////////////////////////////////////////////////////////
__global__ void convolutionConstantMem(
const float* inVec,
float* outVec,
const unsigned int numElements,
const unsigned int numWeights)
{
// perform the convolution using weight array bound to constant memory
performConvolution(inVec, weightsConstantMem, outVec, numElements, numWeights);
}
///////////////////////////////////////////////////////////////////////////////
/// \brief perform convolution using shared memory
///
/// \param [in] inVec the input data vector
/// \param [in] inWeights the input weight vector
/// \param [out] outVec the output data vector
/// \param [in] numElements the number of input and output vector elements
/// \param [in] numWeights the number of weights
///////////////////////////////////////////////////////////////////////////////
__global__ void convolutionSharedMem(
const float* inVec,
const float* inWeights,
float* outVec,
const unsigned int numElements,
const unsigned int numWeights)
{
// load weights into shared memory for each block
extern __shared__ float weightsSharedMem[];
const unsigned int localIdx = threadIdx.x;
if (localIdx < numWeights) {
weightsSharedMem[localIdx] = inWeights[localIdx];
}
__syncthreads();
// after all data is loaded, perform convolution using weights in shared memory
performConvolution(inVec, weightsSharedMem, outVec, numElements, numWeights);
}
///////////////////////////////////////////////////////////////////////////////
/// \brief launch a kernel to perform convolution
///
/// \param [in] kernel the kernel index (i.e., memory type) to use
/// \param [in] blockSize the number of threads per block to use
/// \param [in] inVec the input data vector
/// \param [in] inWeights the input weight vector
/// \param [out] outVec the output data vector
/// \param [in] numElements the number of input and output vector elements
/// \param [in] numWeights the number of weights
///
/// \returns the kernel execution time (in ms)
///////////////////////////////////////////////////////////////////////////////
float launchKernel(
const unsigned int kernel,
const unsigned int blockSize,
const float* inVec,
const float* inWeights,
float* outVec,
const unsigned int numElements,
const unsigned int numWeights)
{
const unsigned int numBlocks = numElements/blockSize;
// start clock and launch kernel
auto start = std::chrono::high_resolution_clock::now();
switch (kernel) {
case 0:
convolutionGlobalMem<<<numBlocks, blockSize>>>(inVec, inWeights, outVec, numElements, numWeights);
break;
case 1:
convolutionConstantMem<<<numBlocks, blockSize>>>(inVec, outVec, numElements, numWeights);
break;
case 2:
convolutionSharedMem<<<numBlocks, blockSize, numWeights*sizeof(float)>>>(inVec, inWeights, outVec, numElements, numWeights);
break;
default:
printf("Invalid kernel index: %d\n", kernel);
}
// calculate execution time in ms
auto stop = std::chrono::high_resolution_clock::now();
std::chrono::duration<float> duration(stop - start);
return duration.count()*1000.0f;
}
///////////////////////////////////////////////////////////////////////////////
/// \brief run a kernel to perform convolution and print timing results
///
/// \param [in] kernel the kernel index (i.e., memory type) to use
/// \param [in] inVec the input data vector
/// \param [in] inWeights the input weight vector
/// \param [out] outVec the output data vector
/// \param [in] numElements the number of input and output vector elements
/// \param [in] numWeights the number of weights
///////////////////////////////////////////////////////////////////////////////
void runTimingTest(
const unsigned int kernel,
const unsigned int blockSize,
const float* inVec,
const float* inWeights,
float* outVec,
const unsigned int numElements,
const unsigned int numWeights)
{
switch (kernel) {
case 0:
printf("Running global memory kernel\n");
break;
case 1:
printf("Running constant memory kernel\n");
break;
case 2:
printf("Running shared memory kernel\n");
break;
default:
printf("Invalid kernel index: %d\n", kernel);
}
float ms = launchKernel(kernel, blockSize, inVec, inWeights, outVec, numElements, numWeights);
printf("Kernel took %.6f ms to run\n", ms);
}
int main(int argc, char** argv)
{
// configure run
unsigned int numElements = 1024;
unsigned int blockSize = 128;
unsigned int numWeights = 8;
if (argc > 1) {
numElements = atoi(argv[1]);
}
if (argc > 2) {
blockSize = atoi(argv[2]);
}
if (argc > 3) {
numWeights = atoi(argv[3]);
}
if (numWeights > MAX_WEIGHTS) {
numWeights = MAX_WEIGHTS;
printf("Warning: numWeights exceeds maximum limit. Setting to %d.\n", numWeights);
}
// allocate memory
const unsigned int dataBytes = numElements*sizeof(float);
const unsigned int weightBytes = numWeights*sizeof(float);
// initialize input data
float* inVec = (float*)malloc(dataBytes);
float* outVec = (float*)malloc(dataBytes);
for (unsigned int i = 0; i < numElements; ++i) {
inVec[i] = 1.0f*i;
}
// initialize weights
float* inWeights = (float*)malloc(weightBytes);
for (unsigned int i = 0; i < numWeights; ++i) {
inWeights[i] = (float)(i+1)/numWeights;
//printf("w[%d] = %.3f\n", i, inWeights[i]);
}
// allocate device memory
float* d_inVec;
float* d_outVec;
float* d_inWeights;
cudaMalloc((void**)&d_inVec, dataBytes);
cudaMalloc((void**)&d_outVec, dataBytes);
cudaMalloc((void**)&d_inWeights, weightBytes);
// initialize weights in constant memory for later
cudaMemcpyToSymbol(weightsConstantMem, inWeights, weightBytes);
// copy data from host to device (kernel does not modify input, so we only have to do this once)
cudaMemcpy(d_inVec, inVec, dataBytes, cudaMemcpyHostToDevice);
cudaMemcpy(d_inWeights, inWeights, weightBytes, cudaMemcpyHostToDevice);
// dummy executions to avoid startup performance hit
for (unsigned int kernel = 0; kernel < 3; ++kernel) {
launchKernel(kernel, blockSize, d_inVec, d_inWeights, d_outVec, numElements, numWeights);
}
// run timing comparisons
for (unsigned int kernel = 0; kernel < 3; ++kernel) {
runTimingTest(kernel, blockSize, d_inVec, d_inWeights, d_outVec, numElements, numWeights);
}
// print output of last kernel
cudaMemcpy(outVec, d_outVec, dataBytes, cudaMemcpyDeviceToHost);
for (unsigned int i = 0; i < numElements; ++i) {
//printf("outVec[%d] = %f\n", i, outVec[i]);
}
// free memory
cudaFree(d_inVec);
cudaFree(d_outVec);
cudaFree(d_inWeights);
free(inVec);
free(inWeights);
free(outVec);
}
|
18,778 | #include <stdio.h>
// For the CUDA runtime routines (prefixed with "cuda_")
#include <cuda_runtime.h>
__global__ void
rng(int *I, int seed)
{
int tx = threadIdx.x;
int ty = threadIdx.y;
int bx = blockIdx.x;
int by = blockIdx.y;
int i = ((by * blockDim.y + ty) * gridDim.x * blockDim.x) + (bx * blockDim.x + tx);
int delta = 0x9E3779B9;
int k0 = 0xA341316C;
int k1 = 0xC8013EA4;
int k2 = 0xAD90777D;
int k3 = 0x7E95761E;
int ITER = 15;
int x = seed;
int y = seed << 3;
x += i + (i << 11) + (i << 19);
y += i + (i << 9) + (i << 21);
int sum = 0;
for (int j=0; j < ITER; j++) {
sum += delta;
x += ((y << 4) + k0) & (y + sum) & ((y >> 5) + k1);
y += ((x << 4) + k2) & (x + sum) & ((x >> 5) + k3);
}
int r = x & 0xFF;
int g = (x & 0xFF00) >> 8;
I[i*4 ] = r;
I[i*4+1] = r;
I[i*4+2] = r;
I[i*4+3] = g;
/*
I[i*4 ] = i;
I[i*4+1] = i;
I[i*4+2] = i;
I[i*4+3] = i;
*/
}
int main(void)
{
// Error code to check return values for CUDA calls
cudaError_t err = cudaSuccess;
// Print the image size to be used, and compute its size in terms of pixels
int seed = 1;
int width = 640;
int height = 480;
int numElements = height * width;
size_t numPixels = 4 * numElements * sizeof(int);
dim3 blocks, threads;
printf("[Random number generation of a %dx%d image]\n", height, width);
// Allocate the host output image
int *h_I = (int *)malloc(numPixels);
// Verify that allocations succeeded
if (h_I == NULL)
{
fprintf(stderr, "Failed to allocate host image!\n");
exit(EXIT_FAILURE);
}
// Allocate the device input image
int *d_I = NULL;
err = cudaMalloc((void **)&d_I, numPixels);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to allocate device image (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
// Launch the Vector Add CUDA Kernel
blocks = dim3(40,30);
threads = dim3(16,16);
printf("CUDA kernel launch with %d blocks of %d threads\n", blocks.x*blocks.y, threads.x*threads.y);
rng<<< blocks, threads >>>(d_I, seed);
err = cudaGetLastError();
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to launch rng kernel (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
printf("Copy output data from the CUDA device to the host memory\n");
err = cudaMemcpy(h_I, d_I, numPixels, cudaMemcpyDeviceToHost);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to copy image data from device to host (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
// Verify that the result image is correct
int *t_I = (int *)malloc(numPixels);
int delta = 0x9E3779B9;
int k0 = 0xA341316C;
int k1 = 0xC8013EA4;
int k2 = 0xAD90777D;
int k3 = 0x7E95761E;
int ITER = 15;
for(int i = 0; i < numElements; i++){
int x = seed;
int y = seed << 3;
x += i + (i << 11) + (i << 19);
y += i + (i << 9) + (i << 21);
int sum = 0;
for (int j=0; j < ITER; j++) {
sum += delta;
x += ((y << 4) + k0) & (y + sum) & ((y >> 5) + k1);
y += ((x << 4) + k2) & (x + sum) & ((x >> 5) + k3);
}
int r = x & 0xFF;
int g = (x & 0xFF00) >> 8;
t_I[i*4 ] = r;
t_I[i*4+1] = r;
t_I[i*4+2] = r;
t_I[i*4+3] = g;
}
for (int i = 0; i < numElements*4; i++)
{
//printf("%d: %d %d\n",i,t_I[i],h_I[i]);
if (fabs(t_I[i] - h_I[i]) > 1e-5)
{
fprintf(stderr, "Result verification failed at element %d!\n", i);
exit(EXIT_FAILURE);
}
}
printf("Test PASSED\n");
// Free device global memory
err = cudaFree(d_I);
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to free device image (error code %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
// Free host memory
free(h_I);
// Reset the device and exit
err = cudaDeviceReset();
if (err != cudaSuccess)
{
fprintf(stderr, "Failed to deinitialize the device! error=%s\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
return 0;
}
|
18,779 | #include <stdio.h>
#include <stdlib.h>
#include <time.h>
__global__ void VecAdd(float* A, float* B, float* C, int N){
int i = threadIdx.x + blockDim.x * blockIdx.x;
if (i < N)
C[i] = A[i] * A[i] + B[i] * B[i];
}
int main(int argc, char** argv){
srand(2634);
int N = atoi(argv[1]);
char* out = argv[2];
cudaEvent_t start, stop;
float dur_time;
size_t size = N * sizeof(float);
float* h_A = (float*)malloc(size);
float* h_B = (float*)malloc(size);
float* h_C = (float*)malloc(size);
int i;
for (i = 0; i < N; ++i){
h_A[i] = (float)rand() / RAND_MAX;
h_B[i] = (float)rand() / RAND_MAX;
}
float* d_A;
cudaMalloc((void**)&d_A, size);
float* d_B;
cudaMalloc((void**)&d_B, size);
float* d_C;
cudaMalloc((void**)&d_C, size);
cudaMemcpy(d_A, h_A, size, cudaMemcpyHostToDevice);
cudaMemcpy(d_B, h_B, size, cudaMemcpyHostToDevice);
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start, 0);
int threadsPerBlock = 256;
int blocksPerGrid = (N + threadsPerBlock - 1) / threadsPerBlock;
VecAdd<<<blocksPerGrid, threadsPerBlock>>>(d_A, d_B, d_C, N);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&dur_time, start, stop);
fprintf(stderr, "%.3f\n", dur_time);
cudaEventDestroy(start);
cudaEventDestroy(stop);
cudaMemcpy(h_C, d_C, size, cudaMemcpyDeviceToHost);
cudaFree(d_A);
cudaFree(d_B);
cudaFree(d_C);
freopen(out, "w", stdout);
for (i = 0; i < N; ++i)
printf("%.7f\n", h_C[i]);
free(h_A);
free(h_B);
free(h_C);
return 0;
} |
18,780 | #include <stdio.h>
__global__ void mykernel(){
}
int main(void) {
mykernel<<<1,1>>>();
printf("Hello World!\n");
return 0;
}
|
18,781 | // System includes
#include <stdio.h>
#include <assert.h>
// CUDA runtime
#include <cuda.h>
#include <cuda_runtime.h>
//#define STREAMS_NUM 8
#define BLOCKSIZE 1024
int STREAMS_NUM;
__global__ void vectorAddGPU(float *a, float *b, float *c, int N, int offset) {
int idx = blockIdx.x*blockDim.x + threadIdx.x + offset;
if (idx < N)
c[idx] = a[idx] + b[idx];
}
void sample_vec_add(int size = 1048576) {
int n = size;
int nBytes = n*sizeof(int);
float *a, *b; // host data
float *c; // results
a = (float *)malloc(nBytes);
b = (float *)malloc(nBytes);
c = (float *)malloc(nBytes);
float *a_d,*b_d,*c_d;
dim3 block(256);
dim3 grid((unsigned int)ceil(n/(float)block.x));
for (int i = 0; i < n; i++) {
a[i] = rand() / (float)RAND_MAX;
b[i] = rand() / (float)RAND_MAX;
c[i] = 0;
}
printf("Allocating device memory on host..\n");
cudaMalloc((void **)&a_d,n*sizeof(float));
cudaMalloc((void **)&b_d,n*sizeof(float));
cudaMalloc((void **)&c_d,n*sizeof(float));
printf("Copying to device..\n");
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start);
cudaMemcpy(a_d,a,n*sizeof(float), cudaMemcpyHostToDevice);
cudaMemcpy(b_d,b,n*sizeof(float), cudaMemcpyHostToDevice);
printf("Doing GPU Vector add\n");
vectorAddGPU<<<grid, block>>>(a_d, b_d, c_d, n, 0);
cudaEventRecord(stop);
cudaEventSynchronize(stop);
float milliseconds = 0;
cudaEventElapsedTime(&milliseconds, start, stop);
printf("time: %f ms\n", milliseconds);
cudaDeviceSynchronize();
cudaFree(a_d);
cudaFree(b_d);
cudaFree(c_d);
}
void streams_vec_add(int size = 1048576) {
int n = size;
int nBytes = n*sizeof(int);
float *a, *b; // host data
float *c; // results
cudaHostAlloc( (void**) &a, n * sizeof(float) ,cudaHostAllocDefault );
cudaHostAlloc( (void**) &b, n * sizeof(float) ,cudaHostAllocDefault );
cudaHostAlloc( (void**) &c, n * sizeof(float) ,cudaHostAllocDefault );
float *a_d,*b_d,*c_d;
for(int i=0; i < n; i++) {
a[i] = rand() / (float)RAND_MAX;
b[i] = rand() / (float)RAND_MAX;
c[i] = 0;
}
printf("Allocating device memory on host..\n");
cudaMalloc((void **)&a_d, n*sizeof(float));
cudaMalloc((void **)&b_d, n*sizeof(float));
cudaMalloc((void **)&c_d, n*sizeof(float));
printf("Copying to device..\n");
cudaEvent_t start, stop;
cudaEventCreate(&start);
cudaEventCreate(&stop);
cudaEventRecord(start);
printf("Doing GPU Vector add\n");
const int StreamSize = n / STREAMS_NUM;
cudaStream_t Stream[STREAMS_NUM];
for (int i = 0; i < STREAMS_NUM; i++)
cudaStreamCreate(&Stream[i]);
dim3 block(BLOCKSIZE);
dim3 grid((StreamSize - 1)/BLOCKSIZE + 1);
for (int i = 0; i < STREAMS_NUM; i++) {
int Offset = i * StreamSize;
cudaMemcpyAsync(&a_d[Offset], &a[Offset], StreamSize * sizeof(float), cudaMemcpyHostToDevice, Stream[i]);
cudaMemcpyAsync(&b_d[Offset], &b[Offset], StreamSize * sizeof(float), cudaMemcpyHostToDevice, Stream[i]);
cudaMemcpyAsync(&c_d[Offset], &c[Offset], StreamSize * sizeof(float), cudaMemcpyHostToDevice, Stream[i]);
vectorAddGPU<<<grid, block>>>(a_d, b_d, c_d, StreamSize, Offset);
cudaMemcpyAsync(&a[Offset], &a_d[Offset], StreamSize * sizeof(float), cudaMemcpyDeviceToHost, Stream[i]);
cudaMemcpyAsync(&b[Offset], &b_d[Offset], StreamSize * sizeof(float), cudaMemcpyDeviceToHost, Stream[i]);
cudaMemcpyAsync(&c[Offset], &c_d[Offset], StreamSize * sizeof(float), cudaMemcpyDeviceToHost, Stream[i]);
}
cudaEventRecord(stop);
cudaEventSynchronize(stop);
float milliseconds = 0;
cudaEventElapsedTime(&milliseconds, start, stop);
printf("time: %f ms\n", milliseconds);
cudaDeviceSynchronize();
cudaFree(a_d);
cudaFree(b_d);
cudaFree(c_d);
cudaFreeHost(a);
cudaFreeHost(b);
cudaFreeHost(c);
}
int main(int argc, char **argv) {
assert(argc==4);
STREAMS_NUM = atoi(argv[3]);
if (atoi(argv[2]) == 0)
sample_vec_add(atoi(argv[1]));
else
streams_vec_add(atoi(argv[1]));
}
|
18,782 |
__global__ void
apply_rows_max(float* X, /** matrix to apply .. row major **/
float* y, /** result vector **/
int* iy,
int rows,
int cols
) {
unsigned int thidx = threadIdx.x;
unsigned int thidy = threadIdx.y;
unsigned int bid = blockIdx.x;
unsigned int bdx = blockDim.x; // assumed equal to blockDim.y .. 16 or 32 ..
unsigned int stride = bdx + 1; // shared mem padded for bank conflicts
unsigned int currow = bdx*bid;
// flexible block size
extern __shared__ float shared_data[];
float *sh_max = shared_data + bdx*stride;
// if( thidy == 0 && thidx + currow < rows ){
// sh_max[thidx] = -1e37;
// }
// __syncthreads();
float cur_val; float new_val; int argmax=0;
for(int chunk = 0; chunk < cols; chunk+=bdx){
// get some values chunking accross rows ...
if(currow+thidy < rows && chunk + thidx < cols){
shared_data[thidx*stride + thidy] = X[(currow + thidy)*cols + chunk + thidx];}
__syncthreads();
// get maximum in chunk ...
if( thidy == 0 && thidx + currow < rows ){
// if first val, it's the max
if( chunk==0 ){
sh_max[thidx] = shared_data[thidx];
}
// get maximmum in chunk ...
for( int i = 0; i < bdx; i++){
if(chunk + i < cols){
cur_val = sh_max[thidx];
new_val = shared_data[i*stride + thidx];
if( cur_val < new_val ){
sh_max[thidx] = new_val;
argmax = chunk + i;
}
}
}
}
__syncthreads();
}
// save values
if(thidx + currow < rows && thidy==0){
y[currow+thidx] = sh_max[thidx];
iy[currow+thidx] = argmax;
}
}
|
18,783 |
// CUDA运行时头文件
#include <cuda_runtime.h>
#include <chrono>
#include <stdio.h>
#include <string.h>
using namespace std;
#define checkRuntime(op) __check_cuda_runtime((op), #op, __FILE__, __LINE__)
bool __check_cuda_runtime(cudaError_t code, const char* op, const char* file, int line){
if(code != cudaSuccess){
const char* err_name = cudaGetErrorName(code);
const char* err_message = cudaGetErrorString(code);
printf("runtime error %s:%d %s failed. \n code = %s, message = %s\n", file, line, op, err_name, err_message);
return false;
}
return true;
}
__global__ void add_vector(const float* a, const float* b, float* c, int count){
int index = blockDim.x * blockIdx.x + threadIdx.x;
if(index >= count) return;
c[index] = a[index] + b[index];
}
__global__ void mul_vector(const float* a, const float* b, float* c, int count){
int index = blockDim.x * blockIdx.x + threadIdx.x;
if(index >= count) return;
c[index] = a[index] * b[index];
}
cudaStream_t stream1, stream2;
float *a, *b, *c1, *c2;
const int num_element = 100000;
const size_t bytes = sizeof(float) * num_element;
const int blocks = 512;
const int grids = (num_element + blocks - 1) / blocks;
const int ntry = 1000;
// 多个流异步
void async(){
cudaEvent_t event_start1, event_stop1;
cudaEvent_t event_start2, event_stop2;
checkRuntime(cudaEventCreate(&event_start1));
checkRuntime(cudaEventCreate(&event_stop1));
checkRuntime(cudaEventCreate(&event_start2));
checkRuntime(cudaEventCreate(&event_stop2));
auto tic = chrono::duration_cast<chrono::microseconds>(chrono::system_clock::now().time_since_epoch()).count() / 1000.0;
checkRuntime(cudaEventRecord(event_start1, stream1));
for(int i = 0; i < ntry; ++i)
add_vector<<<grids, blocks, 0, stream1>>>(a, b, c1, num_element);
checkRuntime(cudaEventRecord(event_stop1, stream1));
checkRuntime(cudaEventRecord(event_start2, stream2));
for(int i = 0; i < ntry; ++i)
add_vector<<<grids, blocks, 0, stream2>>>(a, b, c2, num_element);
checkRuntime(cudaEventRecord(event_stop2, stream2));
checkRuntime(cudaStreamSynchronize(stream1));
checkRuntime(cudaStreamSynchronize(stream2));
auto toc = chrono::duration_cast<chrono::microseconds>(chrono::system_clock::now().time_since_epoch()).count() / 1000.0;
float time1, time2;
checkRuntime(cudaEventElapsedTime(&time1, event_start1, event_stop1));
checkRuntime(cudaEventElapsedTime(&time2, event_start2, event_stop2));
printf("async: time1 = %.2f ms, time2 = %.2f ms, count = %.2f ms\n", time1, time2, toc - tic);
}
// 单个流串行
void sync(){
cudaEvent_t event_start1, event_stop1;
checkRuntime(cudaEventCreate(&event_start1));
checkRuntime(cudaEventCreate(&event_stop1));
auto tic = chrono::duration_cast<chrono::microseconds>(chrono::system_clock::now().time_since_epoch()).count() / 1000.0;
checkRuntime(cudaEventRecord(event_start1, stream1));
for(int i = 0; i < ntry; ++i)
add_vector<<<grids, blocks, 0, stream1>>>(a, b, c1, num_element);
for(int i = 0; i < ntry; ++i)
add_vector<<<grids, blocks, 0, stream1>>>(a, b, c2, num_element);
checkRuntime(cudaEventRecord(event_stop1, stream1));
checkRuntime(cudaStreamSynchronize(stream1));
auto toc = chrono::duration_cast<chrono::microseconds>(chrono::system_clock::now().time_since_epoch()).count() / 1000.0;
float time1;
checkRuntime(cudaEventElapsedTime(&time1, event_start1, event_stop1));
printf("sync: time1 = %.2f ms, count = %.2f ms\n", time1, toc - tic);
}
// 多个流之间并行
void multi_stream_async(){
// 这个案例主要实现多个流之间互相等待，使用event控制实现
// 存在step1 -> step2 \
// -> step3 -> step4
// stepa /
//
// 这个案例中，存在流程1：step1 -> step2的流程
// 存在流程2：stepa
// 存在流程3：step3 -> step4，step3要求step2与stepa作为输入
// 此时，可以让流程1使用stream1，流程2使用stream2，而流程3继续使用stream1，仅仅在stream1中加入等待（event的等待）
// step1 = add_vector
// step2 = mul_vector
// step3 = add_vector
// step4 = mul_vector
// stepa = add_vector
#define step1 add_vector
#define step2 mul_vector
#define step3 add_vector
#define step4 mul_vector
#define stepa add_vector
cudaEvent_t event_async;
checkRuntime(cudaEventCreate(&event_async));
// stream1的执行流程
step1<<<grids, blocks, 0, stream1>>>(a, b, c1, num_element);
step2<<<grids, blocks, 0, stream1>>>(a, b, c1, num_element);
// 等待event_async有事件
checkRuntime(cudaStreamWaitEvent(stream1, event_async));
step3<<<grids, blocks, 0, stream1>>>(a, b, c2, num_element);
step4<<<grids, blocks, 0, stream1>>>(a, b, c2, num_element);
// stream2的执行流程
stepa<<<grids, blocks, 0, stream2>>>(a, b, c2, num_element);
// 为event_async触发事件，通知cudaStreamWaitEvent函数可以继续了
checkRuntime(cudaEventRecord(event_async, stream2));
checkRuntime(cudaStreamSynchronize(stream1));
printf("multi_stream_async done.\n");
}
int main(){
// 本程序实现两个核函数的并行，通过多个流实现
checkRuntime(cudaStreamCreate(&stream1));
checkRuntime(cudaStreamCreate(&stream2));
checkRuntime(cudaMalloc(&a, bytes));
checkRuntime(cudaMalloc(&b, bytes));
checkRuntime(cudaMalloc(&c1, bytes));
checkRuntime(cudaMalloc(&c2, bytes));
// 演示多流之间的异步执行
async();
// 演示单个流内的同步执行
sync();
// 演示多个流之间互相等待的操作
multi_stream_async();
return 0;
} |
18,784 |
/* This is a automatically generated test. Do not modify */
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
__global__
void compute(float comp, int var_1,float var_2,float var_3,float var_4,float var_5,float var_6,float var_7,float var_8,float var_9,float var_10,float var_11,float var_12,float var_13,float var_14,float var_15,float var_16,float var_17,float var_18,float var_19,float var_20) {
comp += +1.1894E25f + var_2 * var_3;
comp = (var_4 / (var_5 + expf(logf(expf((var_6 - (+1.3359E-35f - (var_7 * +1.2098E-43f + (-0.0f - var_8)))))))));
for (int i=0; i < var_1; ++i) {
float tmp_1 = -1.7854E-42f;
comp += tmp_1 * var_9 - -1.2149E-36f - var_10 - (+1.7379E34f + var_11);
comp += -1.1339E-44f - +1.2085E34f;
comp = (var_12 * (var_13 * sinf(-1.2139E-23f)));
}
if (comp >= +1.2367E-37f + -0.0f / (+1.7729E15f / (-1.2644E36f + var_14))) {
comp = fabsf(-0.0f);
}
if (comp <= (+1.4110E34f + -0.0f)) {
comp += var_15 - var_16;
float tmp_2 = +1.5469E-15f;
comp = tmp_2 - (var_17 + cosf((-0.0f + var_18 / ldexpf(+1.8918E36f / sqrtf(-1.3862E-35f + (-1.5382E29f * -1.3392E35f)), 2))));
comp = (var_19 - (+1.5999E-35f / +1.2273E-8f * (var_20 * +0.0f)));
}
printf("%.17g\n", comp);
}
float* initPointer(float v) {
float *ret = (float*) malloc(sizeof(float)*10);
for(int i=0; i < 10; ++i)
ret[i] = v;
return ret;
}
int main(int argc, char** argv) {
/* Program variables */
float tmp_1 = atof(argv[1]);
int tmp_2 = atoi(argv[2]);
float tmp_3 = atof(argv[3]);
float tmp_4 = atof(argv[4]);
float tmp_5 = atof(argv[5]);
float tmp_6 = atof(argv[6]);
float tmp_7 = atof(argv[7]);
float tmp_8 = atof(argv[8]);
float tmp_9 = atof(argv[9]);
float tmp_10 = atof(argv[10]);
float tmp_11 = atof(argv[11]);
float tmp_12 = atof(argv[12]);
float tmp_13 = atof(argv[13]);
float tmp_14 = atof(argv[14]);
float tmp_15 = atof(argv[15]);
float tmp_16 = atof(argv[16]);
float tmp_17 = atof(argv[17]);
float tmp_18 = atof(argv[18]);
float tmp_19 = atof(argv[19]);
float tmp_20 = atof(argv[20]);
float tmp_21 = atof(argv[21]);
compute<<<1,1>>>(tmp_1,tmp_2,tmp_3,tmp_4,tmp_5,tmp_6,tmp_7,tmp_8,tmp_9,tmp_10,tmp_11,tmp_12,tmp_13,tmp_14,tmp_15,tmp_16,tmp_17,tmp_18,tmp_19,tmp_20,tmp_21);
cudaDeviceSynchronize();
return 0;
}
|
18,785 | /**
* For a matrix of size 32 x 32, computes (a_ij)^(pow) for each element a_ij
* and stores in res_ij.
*
* Shared memory is necessary here because we are reading and writing
* to memory many times...
*
* Note that __syncthreads is not needed here because each row in shared
* memory is exclusively read and written to by a single warp.
*/
__global__ void pow_rows(const float *a, uint pow, float *res) {
// store entire matrix in shared memory for fast reads.
__shared__ float s_a[32 * 32];
// store result in shared memory for fast writes.
__shared__ float s_res[32 * 32];
// assign each thread an index so that threads in the same warp process
// elements in the same row.
const uint row_i = threadIdx.x + 32 * threadIdx.y;
// copy matrix from global memory to shared memory in a coalesced fashion.
s_a[row_i] = a[row_i];
// intialize result as a matrix where each element is 1.0.
s_res[row_i] = 1.0;
// a single block computes the power of the entire matrix.
// each warp in the block computes the power of a single row.
// each thread in the warp computes the power of a single element.
while (pow > 0) {
s_res[row_i] *= s_a[row_i];
pow -= 1;
}
// copy result from shared memory to global memory in a coalesced fashion.
res[row_i] = s_res[row_i];
};
/**
* For a matrix of size 32 x 32, computes (a_ij)^(pow) for each element a_ij
* and stores in res_ij.
*
* After reading the matrix a into local memory row by row, we
* compute the power of each element on a column by column basis
* in order to cause a bank conflict.
*
* Note that __syncthreads is necessary here because the same shared
* memory is accessed by multiple warps.
*/
__global__ void pow_cols(const float *a, uint pow, float *res) {
// store entire matrix in shared memory for fast reads.
__shared__ float s_a[32 * 32];
// store result in shared memory for fast writes.
__shared__ float s_res[32 * 32];
// assign each thread an index so that threads in the same warp process
// elements in the same row.
const uint row_i = threadIdx.x + 32 * threadIdx.y;
// copy matrix from global memory to shared memory in a coalesced fashion.
s_a[row_i] = a[row_i];
// intialize result as a matrix where each element is 1.0.
s_res[row_i] = 1.0;
// in order to process the matrix column-by-column... all warps must
// finish initializing shared memory row-by-row.
__syncthreads();
// assign each thread an index so that threads in the same warp process
// elements in the same column.
const uint col_i = threadIdx.y + 32 * threadIdx.x;
// a single block computes the power of the entire matrix.
// each warp in the block computes the power of a single column.
// each thread in the warp computes the power of a single element.
while (pow > 0) {
// Note that col_i % 32 = threadIdx.y.
// Since all threads in the same warp have the same threadIdx.y, this
// is a 32-way bank conflict!
s_res[col_i] *= s_a[col_i];
pow -= 1;
}
// in order to read the matrix row-by-row... all warps must
// finish initializing shared memory column-by-column.
__syncthreads();
// copy result from shared memory to global memory in a coalesced fashion.
res[row_i] = s_res[row_i];
};
/**
* For a matrix of size 32 x 32, computes (a_ij)^(pow) for each element a_ij
* and stores in res_ij.
*
* After reading the matrix a into local memory row by row, we
* compute the power of each element on a column by column basis.
* Due to zero padding, we don't have a bank conflict.
*
* Note that __syncthreads is necessary here because the same shared
* memory is accessed by multiple warps.
*/
__global__ void pow_cols_pad(const float *a, uint pow, float *res) {
// store entire matrix in shared memory for fast reads.
__shared__ float s_a[33 * 33];
// store result in shared memory for fast writes.
__shared__ float s_res[33 * 33];
// assign each thread an index so that threads in the same warp process
// elements in the same column.
const uint row_i = threadIdx.x + 32 * threadIdx.y;
// copy matrix from global memory to shared memory in a coalesced fashion.
s_a[row_i] = a[row_i];
// intialize result as a matrix where each element is 1.0.
s_res[row_i] = 1.0;
// assign each thread an index so that threads in the same warp process
// elements in the same column.
const uint col_i = threadIdx.y + 33 * threadIdx.x;
// in order to process the matrix column-by-column... all warps must
// finish initializing shared memory row-by-row.
__syncthreads();
// a single block computes the power of the entire matrix.
// each warp in the block computes the power of a single column.
// each thread in the warp computes the power of a single element.
while (pow > 0) {
// Results from number theory: Additive group of integers mod n is
// generated by all integers m relatively prime to n. A warp conflict occurs
// if two threads in a warp access the same address mod 32. We
// minimize bank conflicts by reading and writing data to shared memory
// with a stride m relatively prime to n.
//
// Even though we are reading data column-by-column, we don't have
// bank conflicts since our stride is relatively prime to 32.
// For larger matrices (size n), we should choose a stride that is
// relatively prime to 32. It is useful to note that for any integer n,
// gcd(n, n + 1) = 1.
s_res[col_i] *= s_a[col_i];
pow -= 1;
}
// in order to read the matrix row-by-row... all warps must
// finish initializing shared memory column-by-column.
__syncthreads();
// copy result from shared memory to global memory in a coalesced fashion.
res[row_i] = s_res[row_i];
};
|
18,786 | #include "includes.h"
__global__ void stencil2DKernel(double* temperature, double* new_temperature, int block_x, int block_y, int thread_size) {
int i_start = (blockDim.x * blockIdx.x + threadIdx.x) * thread_size + 1;
int i_finish =
(blockDim.x * blockIdx.x + threadIdx.x) * thread_size + thread_size;
int j_start = (blockDim.y * blockIdx.y + threadIdx.y) * thread_size + 1;
int j_finish =
(blockDim.y * blockIdx.y + threadIdx.y) * thread_size + thread_size;
for (int i = i_start; i <= i_finish; i++) {
for (int j = j_start; j <= j_finish; j++) {
if (i <= block_x && j <= block_y) {
new_temperature[j * (block_x + 2) + i] =
(temperature[j * (block_x + 2) + (i - 1)] +
temperature[j * (block_x + 2) + (i + 1)] +
temperature[(j - 1) * (block_x + 2) + i] +
temperature[(j + 1) * (block_x + 2) + i] +
temperature[j * (block_x + 2) + i]) *
DIVIDEBY5;
}
}
}
/* TODO Use shared memory
int i = istart + threadIdx.x + blockDim.x*blockIdx.x;
int j = jstart + threadIdx.y + blockDim.y*blockIdx.y;
if (i < ifinish && j < jfinish) {
__shared__ double shared_temperature[TILE_SIZE][TILE_SIZE];
double center = temperature[j*(block_x+2)+i];
shared_temperature[threadIdx.x][threadIdx.y] = center;
__syncthreads();
// update my value based on the surrounding values
new_temperature[j*(block_x+2)+i] = (
((threadIdx.x > 1) ? shared_temperature[threadIdx.x-1][threadIdx.y] :
temperature[j*(block_x+2)+(i-1)]) +
((threadIdx.x < blockDim.x-1) ?
shared_temperature[threadIdx.x+1][threadIdx.y] :
temperature[j*(block_x+2)+(i+1)]) +
((threadIdx.y > 1) ? shared_temperature[threadIdx.x][threadIdx.y-1] :
temperature[(j-1)*(block_x+2)+i]) +
((threadIdx.y < blockDim.y-1) ?
shared_temperature[threadIdx.x][threadIdx.y+1] :
temperature[(j+1)*(block_x+2)+i]) +
center) * DIVIDEBY5;
}
*/
} |
18,787 |
//In theory, GPU accelerated code
#include <iostream>
#include <math.h>
using namespace std;
__global__ //Kernel function to add the elements of two arrays
void add(int n, float *x, float *y)
{
for(int i= 0; i < n; i++) y[i] = x[i] + y[i]; //Note: i is now the thread index, and each loop through changes to next thread in the block
}
int main(void)
{
int N = 1<<20;
float *x, *y;
//Allovate unified memory - accessible from CPU or GPU
cudaMallocManaged(&x, N*sizeof(float));
cudaMallocManaged(&y, N*sizeof(float));
//Initiallise the x and y arrays on the host
for(int i= 0; i < N; i++)
{
x[i] = 1.0f;
y[i] = 2.0f;
}
//Run kernel on 1M elements on the GPU
add<<<1, 1>>>(N, x, y);
//Wait for GPU to finish before accessing on host
cudaDeviceSynchronize();
float maxError = 0.0f;
for(int i=0; i < N; i++) maxError = fmax(maxError, fabs(y[i] -3.0f));
cout << "Max error: " << maxError << endl;
//Free memory
cudaFree(x);
cudaFree(y);
return 0;
}
|
18,788 | // iamgroot42
// Code for reading files and loading into memory used from the CPU template provided with the assignment
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <math.h>
#include <ctime>
#define LINEWIDTH 20
#define KEYWORD 32
#define CHUNKSIZE 4
__global__ void matchPattern(unsigned int *text, unsigned int *words, int *matches, int length){
__shared__ unsigned int shared_words[CHUNKSIZE+1];
__shared__ unsigned int keywords[KEYWORD];
__shared__ int frequencies[KEYWORD];
unsigned int word, next_word, offset1, offset2, offset3;
int i = (blockIdx.x * blockDim.x) + threadIdx.x;
int match_count = 0;
if(i<length){
// Load keywords, initialize blockwise frequencies
if(threadIdx.x==0){
keywords[threadIdx.y] = words[threadIdx.y];
frequencies[threadIdx.y] = 0;
}
// Load text
if(threadIdx.y==0){
shared_words[threadIdx.x] = text[i];
if(threadIdx.x+1==CHUNKSIZE){
shared_words[CHUNKSIZE] = text[i+1];
}
}
__syncthreads();
// Matching logic
word = shared_words[threadIdx.x];
next_word = shared_words[threadIdx.x+1];
offset1 = (word>>8) + (next_word<<24);
offset2 = (word>>16) + (next_word<<16);
offset3 = (word>>24) + (next_word<<8);
match_count += (word==keywords[threadIdx.y]);
match_count += (offset1==keywords[threadIdx.y]);
match_count += (offset2==keywords[threadIdx.y]);
match_count += (offset3==keywords[threadIdx.y]);
// Increment shared counters
atomicAdd(&frequencies[threadIdx.y], match_count);
__syncthreads();
// Incrememnt global histogram
if(threadIdx.x == 0){
atomicAdd(&matches[threadIdx.y], frequencies[threadIdx.y]);
}
}
}
int main(){
int length, len, matches[KEYWORD];
int* cuda_matches;
char *ctext, keywords[KEYWORD][LINEWIDTH], *line;
cudaMallocHost(&line, sizeof(char)*LINEWIDTH);
unsigned int *text, *words;
unsigned int *cuda_text, *cuda_words;
memset(matches, -1, sizeof(matches));
// read in text and keywords for processing
FILE *fp, *wfile;
wfile = fopen("./data/keywords.txt","r");
if (!wfile){ printf("keywords.txt: File not found.\n"); exit(0);}
int k=0, cnt = KEYWORD;
size_t read, linelen = LINEWIDTH;
while((read = getline(&line, &linelen, wfile)) != -1 && cnt--){
strncpy(keywords[k], line, sizeof(line));
keywords[k][4] = '\0';
k++;
}
fclose(wfile);
fp = fopen("./data/large.txt","r");
if (!fp){ printf("Unable to open the file.\n"); exit(0);}
length = 0;
while (getc(fp) != EOF) length++;
cudaMallocHost(&ctext, length+4);
rewind(fp);
for (int l=0; l<length; l++) ctext[l] = getc(fp);
for (int l=length; l<length+4; l++) ctext[l] = ' ';
fclose(fp);
// define number of words of text, and set pointers
len = length/4;
text = (unsigned int *) ctext;
// define words for matching
cudaMallocHost(&words, KEYWORD*sizeof(unsigned int));
for (int w=0; w<KEYWORD; w++){
words[w] = ((unsigned int) keywords[w][0])
+ ((unsigned int) keywords[w][1])*(1<<8)
+ ((unsigned int) keywords[w][2])*(1<<16)
+ ((unsigned int) keywords[w][3])*(1<<24);
}
cudaMalloc((void**) &cuda_text, length+4);
cudaMalloc((void**) &cuda_words, KEYWORD*sizeof(unsigned int));
cudaMalloc((void**) &cuda_matches, sizeof(matches));
cudaMemcpy(cuda_text, text, length+4, cudaMemcpyHostToDevice);
cudaMemcpy(cuda_words, words, KEYWORD*sizeof(unsigned int), cudaMemcpyHostToDevice);
cudaMemset(cuda_matches, 0, sizeof(matches));
cudaMemset(cuda_matches, 0, sizeof(matches));
dim3 threadsPerBlock(CHUNKSIZE, KEYWORD);
dim3 numBlocks(len/CHUNKSIZE);
matchPattern<<<numBlocks, threadsPerBlock>>>(cuda_text, cuda_words, cuda_matches, len);
cudaMemcpy(matches, cuda_matches, sizeof(matches), cudaMemcpyDeviceToHost);
printf("Printing Matches:\n");
printf("Word\t |\tNumber of Matches\n===================================\n");
for (int i = 0; i < KEYWORD; ++i)
printf("%s\t |\t%d\n", keywords[i], matches[i]);
cudaFree(ctext);
cudaFree(words);
cudaFree(line);
cudaFree(cuda_text);
cudaFree(cuda_words);
cudaFree(cuda_matches);
return 0;
}
|
18,789 | #include "includes.h"
__global__ void MatrixMulKernel(int *d_x, int *d_y, int *d_z, int Block_Width, int M , int N) {
int row = blockIdx.y*blockDim.y+ threadIdx.y;
int col = blockIdx.x*blockDim.x+ threadIdx.x;
int kernelSum = 0;
if ((row<N) && (col<N)) {
for (int i = 0; i < Block_Width ; ++i) {
kernelSum+=d_x[col * Block_Width + i] * d_y[i * Block_Width + row];
}
}
d_z[row * Block_Width +col] = kernelSum;
} |
18,790 | #include <math.h>
__global__
void kernel1(const float *input, const float *input2, float *output, int dataSize)
{
int blockNum = blockIdx.z*(gridDim.x*gridDim.y) + blockIdx.y*gridDim.x + blockIdx.x;
int threadNum = threadIdx.z*(blockDim.x*blockDim.y) + threadIdx.y*(blockDim.x) + threadIdx.x;
int i = blockNum*(blockDim.x*blockDim.y*blockDim.z) + threadNum;
if (i < dataSize)
{
output[i] = sin(input[i]) + cos(input2[i]);
}
}
__global__
void kernel2(const float *input, float *output, int dataSize)
{
int blockNum = blockIdx.z*(gridDim.x*gridDim.y) + blockIdx.y*gridDim.x + blockIdx.x;
int threadNum = threadIdx.z*(blockDim.x*blockDim.y) + threadIdx.y*(blockDim.x) + threadIdx.x;
int i = blockNum*(blockDim.x*blockDim.y*blockDim.z) + threadNum;
if (i < dataSize)
{
output[i] = log(input[i]);
}
}
__global__
void kernel3(const float *input, float *output, int dataSize)
{
int blockNum = blockIdx.z*(gridDim.x*gridDim.y) + blockIdx.y*gridDim.x + blockIdx.x;
int threadNum = threadIdx.z*(blockDim.x*blockDim.y) + threadIdx.y*(blockDim.x) + threadIdx.x;
int i = blockNum*(blockDim.x*blockDim.y*blockDim.z) + threadNum;
if (i < dataSize)
{
output[i] = sqrt(input[i]);
}
}
|
18,791 | #include "includes.h"
__global__ void MSD_GPU_Interpolate_linear(float *d_MSD_DIT, float *d_MSD_interpolated, int *d_MSD_DIT_widths, int MSD_DIT_size, int *boxcar, int max_width_performed){
int tid = threadIdx.x;
if(boxcar[tid] <= max_width_performed) {
// int f = threadIdx.x;
int desired_width = boxcar[tid];
int position = (int) floorf(log2f((float) desired_width));
float width1 = d_MSD_DIT_widths[position];
float mean1 = d_MSD_DIT[(position)*MSD_RESULTS_SIZE];
float StDev1 = d_MSD_DIT[(position)*MSD_RESULTS_SIZE +1];
// printf("\nBoxcar: %f \t desired: %f", (float)boxcar[f], desired_width);
if(position == MSD_DIT_size-1 && width1==(int) desired_width) {
// (*mean) = mean1;
// (*StDev) = StDev1;
d_MSD_interpolated[tid*2] = mean1;
d_MSD_interpolated[tid*2+1] = StDev1;
}
else {
float width2 = d_MSD_DIT_widths[position+1];
float distance_in_width = width2 - width1;
float mean2 = d_MSD_DIT[(position+1)*MSD_RESULTS_SIZE];
float distance_in_mean = mean2 - mean1;
float StDev2 = d_MSD_DIT[(position+1)*MSD_RESULTS_SIZE +1];
float distance_in_StDev = StDev2 - StDev1;
// printf("Position: \t %i \t f: %i\n", position, f);
// printf("width:[%f;%f]; mean:[%f;%f]; sd:[%f;%f]\n",width1, width2, mean1, mean2, StDev1, StDev2);
// printf("d width %f; d mean: %f; d StDef: %f\n", distance_in_width, distance_in_mean, distance_in_StDev);
// printf("\tDesired_width: %f\n", desired_width);
// (*mean) = mean1 + (distance_in_mean/distance_in_width)*((float) desired_width - width1);
// (*StDev) = StDev1 + (distance_in_StDev/distance_in_width)*((float) desired_width - width1);
d_MSD_interpolated[tid*2] = mean1 + (distance_in_mean/distance_in_width)*((float) desired_width - width1);
d_MSD_interpolated[tid*2+1] = StDev1 + (distance_in_StDev/distance_in_width)*((float) desired_width - width1);
}
}
} |
18,792 | #include <iostream>
#include <cuda.h>
using std::cout;
using std::endl;
__global__ void add_me(int *a, int* b, int *c) {
if(threadIdx.x < 8)
c[threadIdx.x] = a[threadIdx.x] + b[threadIdx.x];
}
int main(int argc, char *argv[])
{
int arr1[8] = {1, 2, 3, 4 , 5 ,6 ,7, 8};
int arr2[8] = {9, 10, 11, 12, 13, 14, 15, 16};
cout << "First array: " << endl;
for (int x: arr1) {
cout << x << endl;
}
cout << endl << "Second array: " << endl;
for (int x: arr2) {
cout << x << endl;
}
int *d_a;
int *d_b;
int *d_c;
int h_sum[8];
cudaMalloc((void**)&d_a, 8 * sizeof(int));
cudaMalloc((void**)&d_b, 8 * sizeof(int));
cudaMalloc((void**)&d_c, 8 * sizeof(int));
cudaMemcpy(d_a, arr1, 8 * sizeof(int), cudaMemcpyHostToDevice);
cudaMemcpy(d_b, arr2, 8 * sizeof(int), cudaMemcpyHostToDevice);
add_me<<<1, 16>>>(d_a, d_b, d_c);
cudaMemcpy(h_sum, d_c, 8 * sizeof(int), cudaMemcpyDeviceToHost);
cout << endl << "First array + second array: " << endl;
for (int ii = 0; ii < 8; ii++)
cout << h_sum[ii] << endl;
cudaFree(d_a);
cudaFree(d_b);
cudaFree(d_c);
return 0;
}
|
18,793 | #include <iostream>
using std::cerr;
using std::endl;
// Error handling macro
#define CUDA_CHECK(call) \
if((call) != cudaSuccess) { \
cudaError_t err = cudaGetLastError(); \
cerr << "CUDA error calling \""#call"\", code is " << err << endl;}
#include<stdio.h>
#include<stdlib.h>
void init_mtx(float* mtx, int n_unknows){
for(int i=0; i<n_unknows; i++){
for (int j=0; j<(n_unknows+1); j++){
int mp = i*(n_unknows+1) + j;
mtx[mp] = (float)(rand()%10);
}
}
}
void gauss_solver(float* mtx, int const n_unknows){
for (int i=0; i<1; i++){
for (int j=i+1; j<n_unknows; j++){
int const mp = i*(n_unknows+1) + i;
float ratio = mtx[j*(n_unknows+1)+i]/mtx[mp];
for ( int k=0; k<n_unknows; k++){
mtx[j*(n_unknows+1)+k] -= ratio*mtx[i*(n_unknows+1)+k];
}
}
}
}
void print_mtx(float* mtx, int n_r, int n_c){
if (n_c>12 || n_c> 12) {
printf("too large to be printed");
return;
}
for(int i=0; i<n_r; i++){
for(int j=0; j<n_c; j++){
int mp = i*n_c + j;
printf("%6.2f ", mtx[mp]);
}
printf("\n");
}
printf("-------------------------------------\n");
}
int main() {
float* arg_mtx;
float* h_mtx;
int const n_unknows = 8;
size_t mSize = n_unknows*(n_unknows+1)*sizeof(float);
CUDA_CHECK(cudaMallocManaged((void**)&arg_mtx, mSize));
h_mtx = (float*)malloc(mSize);
init_mtx(h_mtx, n_unknows);
print_mtx(h_mtx, n_unknows, n_unknows+1);
gauss_solver(h_mtx, n_unknows);
print_mtx(h_mtx, n_unknows, n_unknows+1);
return 0;
}
|
18,794 | #include <stdio.h>
#include <math.h>
#define MAX 8192
#define LOG_MAX 13
#define N 10
const int BLOCK_SIZE = 512;
void bit_reverse(float x_r[], float x_i[]);
__host__ void fftHost(float *x_r, float *x_i);
__global__ void fftKernel(float *dx_r, float *dx_i);
int main() {
float *x_r, *x_i, *xr, *xi;
float *dx_r, *dx_i;
int i;
int correct_flag = 1;
dim3 dim_grid(1, 1), dim_block(BLOCK_SIZE, 1, 1);
cudaEvent_t start, stop;
float elapsed_time;
//Step 0. Timer
cudaEventCreate(&start);
cudaEventCreate(&stop);
x_r = (float *)malloc(sizeof(float) * MAX);
x_i = (float *)malloc(sizeof(float) * MAX);
xr = (float *)malloc(sizeof(float) * MAX);
xi = (float *)malloc(sizeof(float) * MAX);
//initialization
for (i = 0; i < MAX; i++) {
x_r[i] = cos(N * 2 * M_PI * i / MAX);
x_i[i] = 0;
}
bit_reverse(x_r, x_i);
cudaEventRecord(start, 0);
fftHost(x_r, x_i);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&elapsed_time, start, stop);
for (i = 0; i < MAX; i++) {
if (i == N || i == MAX - N) {
if (round(x_r[i]) != MAX/2 || round(x_i[i]) != 0)
correct_flag = 0;
} else {
if (round(x_r[i]) != 0 || round(x_i[i]) != 0)
correct_flag = 0;
}
}
if (correct_flag == 1) {
printf("CPU time[sec]:%lf\n", elapsed_time);
} else {
fprintf(stderr, "CPU Failed\n");
}
//GPU initialization again
for (i = 0; i < MAX; i++) {
x_r[i] = cos(N * 2 * M_PI * i / MAX);
x_i[i] = 0;
}
cudaMalloc((void **)&dx_r, sizeof(float) * MAX);
cudaMalloc((void **)&dx_i, sizeof(float) * MAX);
bit_reverse(x_r, x_i);
cudaMemcpy(dx_r, x_r, sizeof(float) * MAX, cudaMemcpyHostToDevice);
cudaMemcpy(dx_i, x_i, sizeof(float) * MAX, cudaMemcpyHostToDevice);
cudaMemcpy(xr, dx_r, sizeof(float) * MAX, cudaMemcpyDeviceToHost);
cudaMemcpy(xi, dx_i, sizeof(float) * MAX, cudaMemcpyDeviceToHost);
cudaEventRecord(start, 0);
fftKernel<<<dim_grid, dim_block>>>(dx_r, dx_i);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&elapsed_time, start, stop);
cudaMemcpy(x_r, dx_r, sizeof(float) * MAX, cudaMemcpyDeviceToHost);
cudaMemcpy(x_i, dx_i, sizeof(float) * MAX, cudaMemcpyDeviceToHost);
cudaFree(dx_r);
cudaFree(dx_i);
correct_flag=1;
for (i = 0; i < MAX; i++) {
if (i == N || i == MAX - N) {
if (round(x_r[i]) != MAX/2 || round(x_i[i]) != 0)
correct_flag = 0;
} else {
if (round(x_r[i]) != 0 || round(x_i[i]) != 0)
correct_flag = 0;
}
}
if (correct_flag == 1) {
printf("GPU time[sec]:%lf\n", elapsed_time);
} else {
printf("GPU Failed:%lf\n", elapsed_time);
fprintf(stderr, "GPU Failed:%lf\n", elapsed_time);
}
}
unsigned int reverse_bits(unsigned int input) {
unsigned int rev = 0;
int i;
for (i = 0; i < LOG_MAX; i++) {
rev = (rev << 1) | (input & 1);
input = input >> 1;
}
return rev;
}
void bit_reverse(float x_r[], float x_i[]) {
unsigned int reversed, i;
float tmp;
for (i = 0; i < MAX; i++) {
reversed = reverse_bits(i);
if (i < reversed) {
tmp = x_r[i];
x_r[i] = x_r[reversed];
x_r[reversed] = tmp;
tmp = x_i[i];
x_i[i] = x_i[reversed];
x_i[reversed] = tmp;
}
}
}
|
18,795 | #include<stdio.h>
#include<stdlib.h>
#include<math.h>
#define BLOCK_SIZE 3
int w=3, h=3;
int size = w*h;
int memsize = sizeof(float)*size;
__global__ void matrixMultiply(float *a, float *b, float *c, int w, int h){
int tx = (blockIdx.x * blockDim.x) + threadIdx.x;
int ty = (blockIdx.y * blockDim.y) + threadIdx.y;
float v = 0;
int i;
for(i=0; i<w; i++){
v+= a[ty*w + i]*b[i*h + tx];
}
c[ty*w + tx] = v;
}
void printMatrix(float *a){
int i;
for(i=0; i<size; i++){
printf("%f\t", a[i]);
if((i+1)%w == 0)
printf("\n");
}
}
int main(int argc, char** argv){
float *ha, *hb, *hc, *a, *b, *c;
int i;
ha = (float*)malloc(memsize);
hb = (float*)malloc(memsize);
hc = (float*)malloc(memsize);
for(i=0; i<size; i++)
ha[i] = hb[i] = i;
printMatrix(ha);
printMatrix(hb);
cudaMalloc(&a, memsize);
cudaMalloc(&b, memsize);
cudaMalloc(&c, memsize);
cudaMemcpy(a, ha, memsize, cudaMemcpyHostToDevice);
cudaMemcpy(b, hb, memsize, cudaMemcpyHostToDevice);
dim3 threads(BLOCK_SIZE, BLOCK_SIZE);
dim3 grid(w/threads.x, h/threads.y);
matrixMultiply<<<grid,threads>>>(a, b, c, w, w);
cudaMemcpy(hc, c, memsize, cudaMemcpyDeviceToHost);
printMatrix(hc);
cudaFree(a);
cudaFree(b);
cudaFree(c);
free(ha);
free(hb);
free(hc);
printf("DONE");
return 0;
} |
18,796 | #include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <cuda_runtime_api.h>
#define BASE_TYPE float
__global__ void add(BASE_TYPE *a, BASE_TYPE *b, BASE_TYPE *result, const int N)
{
int threads_count = blockDim.x * gridDim.x;
int elem_per_thread = N / threads_count;
int k = (blockIdx.x * blockDim.x + threadIdx.x) * elem_per_thread;
for (int i = k; i < k + elem_per_thread; i++)
{
result[i] = a[i] + b[i];
}
}
BASE_TYPE* gen_array(const int N)
{
BASE_TYPE *a = new BASE_TYPE[N];
for (int i = 0; i < N; i++)
a[i] = rand() % 100;
return a;
}
void print_array(const BASE_TYPE *a, const int N)
{
for (int i = 0; i < N; i++)
printf("%3.0f ", a[i]);
printf("\n");
}
void cuda_init_array(BASE_TYPE **dev, const BASE_TYPE *host, const size_t size)
{
cudaError_t err;
err = cudaMalloc((void **)dev, size);
if (err != cudaSuccess)
throw err;
if (host != NULL)
{
err = cudaMemcpy(*dev, host, size, cudaMemcpyHostToDevice);
if (err != cudaSuccess)
throw err;
}
}
void cuda_init_grid_and_block(dim3 *grid, dim3 *block, const int N, const int threads_per_block)
{
int blocks_count = N / threads_per_block;
*grid = dim3(blocks_count);
*block = dim3(threads_per_block);
printf("Block %d %d %d\n", block->x, block->y, block->z);
printf("Grid %d %d %d\n", grid->x, grid->y, grid->z);
}
int main()
{
srand(time(NULL));
const int N = 32768;
const size_t size = N * sizeof(BASE_TYPE);
int threads_per_block;
scanf("%d", &threads_per_block);
dim3 threadsPerBlock, blocksPerGrid;
cuda_init_grid_and_block(&threadsPerBlock, &blocksPerGrid, N, threads_per_block);
cudaEvent_t start, stop;
float h2d_cp_span, d2h_cp_span, k_span;
cudaEventCreate(&start);
cudaEventCreate(&stop);
BASE_TYPE *host_a = gen_array(N), *host_b = gen_array(N), *host_c = new BASE_TYPE[N];
BASE_TYPE *dev_a, *dev_b, *dev_c;
if (host_a == NULL || host_b == NULL || host_c == NULL)
{
fprintf(stderr, "Failed to allocate host vectors!\n");
exit(EXIT_FAILURE);
}
cudaEventRecord(start, 0);
try
{
cuda_init_array(&dev_a, host_a, size);
cuda_init_array(&dev_b, host_b, size);
cuda_init_array(&dev_c, NULL, size);
}
catch (cudaError_t err)
{
fprintf(stderr, "Failed to allocate device (error code: %s)!\n", cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&h2d_cp_span, start, stop);
for(int i = 0; i < 100; i++)
add<<<blocksPerGrid, threadsPerBlock>>>(dev_a, dev_b, dev_c, N);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&k_span, start, stop);
cudaMemcpy(host_c, dev_c, size, cudaMemcpyDeviceToHost);
cudaEventRecord(stop, 0);
cudaEventSynchronize(stop);
cudaEventElapsedTime(&d2h_cp_span, start, stop);
// printf("Copy form host to device time: %.2f milliseconds\n", h2d_cp_span);
printf("Run kernel time: %.2f milliseconds\n", (k_span - h2d_cp_span) / 100);
// printf("Copy form device to host time: %.2f milliseconds\n", d2h_cp_span);
cudaFree(dev_a);
cudaFree(dev_b);
cudaFree(dev_c);
delete[] host_a;
delete[] host_b;
delete[] host_c;
return 0;
} |
18,797 | #include "includes.h"
// helper for CUDA error handling
__global__ void getLowerAAt( const double* A, double* S, std::size_t imageNum, std::size_t pixelNum )
{
std::size_t row = blockIdx.x;
std::size_t col = blockIdx.y * blockDim.x + threadIdx.x;
if(row >= imageNum || col >= imageNum)
{
return;
}
S[row * imageNum + col] = 0.0;
for(std::size_t i = 0; i < pixelNum; ++i)
{
S[row * imageNum + col] += A[row * pixelNum + i] * A[col * pixelNum + i];
}
} |
18,798 | /**
* Alemdar Salmoor
* */
#include <stdlib.h>
#include <stdio.h>
#include <time.h>
#include <string.h>
#include <limits.h>
#include <float.h>
#include <math.h>
//The following implementation of the atomicAdd for devices with compute capabilities lower
//than 6.0 is provided on the NVidia Cuda Toolkit Documentation page.
__device__ double myAtomicAdd(double* address, double val)
{
unsigned long long int* address_as_ull =
(unsigned long long int*)address;
unsigned long long int old = *address_as_ull, assumed;
do {
assumed = old;
old = atomicCAS(address_as_ull, assumed,
__double_as_longlong(val +
__longlong_as_double(assumed)));
// Note: uses integer comparison to avoid hang in case of NaN (since NaN != NaN)
} while (assumed != old);
return __longlong_as_double(old);
}
void arrayGenerator(int * arr, int N);
double dotProduct(int * A, int * B, int N);
double serial(int * A, int * B, int N);
double parallel(int * A, int * B, int N, int, int *, double *, double *, double *, double *);
void output(int, int, int, double, double, double, double, double, double, double, double);
double computeTime(clock_t dif);
int myMin(int, int);
void computeBlocksAndSmallestArray(int, int, int, int, int, int *, int *, int *);
int getMaxBlocks(int, int);
void myPrintDash(int range);
void readArrays(char *, int ** , int **, int *);
double firstLargerN(int target);
int computeExtraBlocks(int N, int base, int blockSize, int intsPerThread);
__global__ void angleKernel(int * A, int * B, int N, double * dab, double * daa, double * dbb, int);
int main(int argc, char ** argv){
//Seed
srand(time(NULL));
//Time
clock_t mTime;
int N = atoi(argv[1]);
int blockSize = atoi(argv[2]);
char * input;
//Output
int elems, tpb, blocks;
double tAG, tCPU, tHDT, tKernel, tDHT, tGPU;
double rCPU, rGPU;
elems = N;
tpb = blockSize;
int * A;
int * B;
mTime = clock();
if(argc == 4){
input = argv[3];
readArrays(input, &A, &B, &N);
}
else{
A = (int *) malloc(N * sizeof(int));
B = (int *) malloc(N * sizeof(int));
arrayGenerator(A, N);
arrayGenerator(B, N);
}
mTime = clock() - mTime;
tAG = computeTime(mTime);
mTime = clock();
rCPU = serial(A, B, N);
mTime = clock() - mTime;
tCPU = computeTime(mTime);
rGPU = parallel(A, B, N, blockSize, &blocks, &tHDT, &tKernel, &tDHT, &tGPU);
free(A);
free(B);
output(elems, tpb, blocks, tAG, tCPU, tHDT, tKernel, tDHT, tGPU, rCPU, rGPU);
return 0;
}
void arrayGenerator(int *arr, int N){
for (size_t i = 0; i < N; i++)
{
arr[i] = rand();
arr[i] = arr[i] - RAND_MAX/2;
}
}
double serial(int * A, int * B, int N){
double numerator, denominator, A_squared, B_squared;
double cos_angle, angle;
numerator = dotProduct(A, B, N);
A_squared = dotProduct(A, A, N);
B_squared = dotProduct(B, B, N);
denominator = A_squared * B_squared;
denominator = sqrt(denominator);
cos_angle = numerator/denominator;
angle = acos(cos_angle);
angle = angle * (180.0/M_PI);
return angle;
}
double dotProduct(int * A, int * B, int N){
double product = 0.0;
for (size_t i = 0; i < N; i++){
product += (double) A[i] * (double) B[i];
}
return product;
}
void output(int elems, int tpb, int blocks, double tAG, double tCPU, double tHDT, double tKernel, double tDHT, double tGPU, double rCPU, double rGPU){
printf("\n");
printf("Info\n");
myPrintDash(14);
printf("Number of elements: %d\n", elems);
printf("Number of threads per block: %d\n", tpb);
printf("Number of blocks will be created: %d\n", blocks);
printf("\n");
printf("Time\n");
myPrintDash(14);
printf("Time for the array generation: %lf ms\n", tAG);
printf("Time for the CPU function: %lf ms\n", tCPU);
printf("Time for the Host to Device transfer: %lf ms\n", tHDT);
printf("Time for the kernel execution: %lf ms\n", tKernel);
printf("Time for the Device to Host transfer: %lf ms\n", tDHT);
printf("Total execution time for GPU: %lf ms\n", tGPU);
printf("\n");
printf("Results\n");
myPrintDash(14);
printf("CPU result: %.3lf\n", rCPU);
printf("GPU result: %.3lf\n", rGPU);
printf("\n");
}
double computeTime(clock_t dif){
double e = (double) dif;
e = e/CLOCKS_PER_SEC;
e = e * 1000.0;
return e;
}
double parallel(int * A, int * B, int N, int blockSize, int * blocksCreated, double * tHDT, double * tKernel, double * tDHT, double * tGPU){
clock_t mTime;
cudaDeviceProp p;
cudaGetDeviceProperties(&p, 0);
int multiProcs = p.multiProcessorCount;
int maxThreads = p.maxThreadsPerMultiProcessor;
int major = p.major;
int minor = p.minor;
int maxBlocks = getMaxBlocks(major, minor);
int smallestArraySize, numBlocks, INTS_PER_THREAD;
computeBlocksAndSmallestArray(multiProcs, blockSize, maxBlocks, maxThreads, N, &smallestArraySize, &numBlocks, &INTS_PER_THREAD);
//output variables
double numerator, A_squared, B_squared;
//device arrays;
int * D_A;
int * D_B;
double * dotAB;
double * dotAA;
double * dotBB;
//Allocate for device
cudaMalloc(&D_A, smallestArraySize * sizeof(int));
cudaMalloc(&D_B, smallestArraySize * sizeof(int));
cudaMalloc(&dotAB, sizeof(double));
cudaMalloc(&dotAA, sizeof(double));
cudaMalloc(&dotBB, sizeof(double));
size_t size = N * sizeof(int);
//copy from host to device
mTime = clock();
cudaMemcpy(D_A, A, size, cudaMemcpyHostToDevice);
cudaMemcpy(D_B, B, size, cudaMemcpyHostToDevice);
mTime = clock() - mTime;
(*tHDT) = computeTime(mTime);
int surplus = (smallestArraySize - N) * sizeof(int);
cudaMemset(dotAA, 0, sizeof(double));
cudaMemset(dotAB, 0, sizeof(double));
cudaMemset(dotBB, 0, sizeof(double));
cudaMemset((D_A + N), 0, surplus);
cudaMemset((D_B + N), 0, surplus);
int sharedMem = 3 * sizeof(double) * blockSize;
mTime = clock();
angleKernel<<<numBlocks, blockSize, sharedMem>>>(D_A, D_B, N, dotAB, dotAA, dotBB, INTS_PER_THREAD);
cudaDeviceSynchronize();
mTime = clock() - mTime;
(*tKernel) = computeTime(mTime);
//copy from device to host
mTime = clock();
cudaMemcpy(&numerator, dotAB, sizeof(double), cudaMemcpyDeviceToHost);
cudaMemcpy(&A_squared, dotAA, sizeof(double), cudaMemcpyDeviceToHost);
cudaMemcpy(&B_squared, dotBB, sizeof(double), cudaMemcpyDeviceToHost);
mTime = clock() - mTime;
(*tDHT) = computeTime(mTime);
double denominator = A_squared * B_squared;
denominator = sqrt(denominator);
double cos_angle = numerator/denominator;
double angle = acos(cos_angle);
angle = angle * (180.0/M_PI);
(*tGPU) = (*tHDT) + (*tKernel) + (*tDHT);
(*blocksCreated) = numBlocks;
cudaFree(D_A);
cudaFree(D_B);
cudaFree(dotAB);
cudaFree(dotAA);
cudaFree(dotBB);
return angle;
}
__global__ void angleKernel(int * A, int * B, int N, double * dotAB, double * dotAA, double * dotBB, int intsPerThread){
extern __shared__ double sharedMem[];
int gid = threadIdx.x * intsPerThread + blockIdx.x * blockDim.x * intsPerThread;
int tid = threadIdx.x;
int gend = gid + intsPerThread;
double * localAB = sharedMem;
double * localAA = (localAB + blockDim.x);
double * localBB = (localAA + blockDim.x);
double Aval, Bval;
localAB[tid] = 0.0; localAA[tid] = 0.0; localBB[tid] = 0.0;
for(int i = gid; i < gend; i++){
Aval = (double) A[i];
Bval = (double) B[i];
localAB[tid] += Aval * Bval;
localAA[tid] += Aval * Aval;
localBB[tid] += Bval * Bval;
}
int size = blockDim.x / 2;
while(size > 0){
__syncthreads();
if(tid < size){
localAB[tid] += localAB[tid + size];
localAA[tid] += localAA[tid + size];
localBB[tid] += localBB[tid + size];
}
size = size/2;
}
if(tid == 0){
myAtomicAdd(dotAB, localAB[tid]);
myAtomicAdd(dotAA, localAA[tid]);
myAtomicAdd(dotBB, localBB[tid]);
}
}
void computeBlocksAndSmallestArray(int multiProcs, int blockSize, int maxBlocks, int maxThreads, int N, int * smallestArraySize, int * numBlocks, int * INTS_PER_THREAD){
int maxThreadChunk = maxThreads/blockSize;
int activeBlocks = myMin(maxBlocks, maxThreadChunk);
int base = multiProcs * activeBlocks;
double largerN = firstLargerN(N);
double logN = log2(largerN);
(* INTS_PER_THREAD) = (int) ceil(logN);
int extraBlocks = computeExtraBlocks(N, base, blockSize, (* INTS_PER_THREAD));
(* numBlocks) = base + extraBlocks;
(* smallestArraySize) = (* numBlocks) * blockSize * (* INTS_PER_THREAD);
}
int getMaxBlocks(int major, int minor){
int maxBlocks = 16;
if(major == 5 || major == 6 || (major == 7 && minor == 0)){
maxBlocks = 32;
}
return maxBlocks;
}
int myMin(int A, int B){
if(A < B) return A;
else return B;
}
void myPrintDash(int range){
for(int i = 0; i < range; i++){
printf("\u2012");
}
printf("\n");
}
void readArrays(char * input, int ** A, int ** B, int * N){
FILE * finput = fopen(input, "r");
fscanf(finput, "%d\n", N);
int num, i;
(* A) = (int *) malloc((* N) * sizeof(int));
(* B) = (int *) malloc((* N) * sizeof(int));
for(i = 0; i < (* N); i++){
fscanf(finput, "%d\n", &num);
(* A)[i] = num;
}
for(i = 0; i < (* N); i++){
fscanf(finput, "%d\n", &num);
(* B)[i] = num;
}
fclose(finput);
}
double firstLargerN(int t){
double target = (double) t;
double accumulator = 1;
while(accumulator < target){
accumulator *= 2;
}
return accumulator;
}
int computeExtraBlocks(int N, int base, int blockSize, int intsPerThread){
double dN = (double) N;
double dBase = (double) base;
double dBlockSize = (double) blockSize;
double ipt = (double) intsPerThread;
double result = dN/(dBlockSize * ipt) - dBase;
return (int) ceil(result);
}
|
18,799 | /*
* Matrix is a PHP extension. It can do parallel computing base on CUDA.
*
* GitHub: https://github.com/BourneSuper/matrix
*
* Author: Bourne Wong <cb44606@gmail.com>
*
* */
#include <stdio.h>
// For the CUDA runtime routines (prefixed with "cuda_")
#include <cuda_runtime.h>
#include "math.cuh"
int getMaxThreadsPerMultiProcessor( deviceContextStruct * deviceContextStructP ){
cudaDeviceProp deviceProp;
cudaGetDeviceProperties( &deviceProp, deviceContextStructP->deviceId );
return deviceProp.maxThreadsPerMultiProcessor;
}
//arrayAdd()
__global__ void arrayAddKernel( double *deviceA, double alpha, int elementNum ){
int i = blockDim.x * blockIdx.x + threadIdx.x;
if( i < elementNum) {
deviceA[i] = deviceA[i] + alpha;
}
}
void arrayAdd( deviceContextStruct * deviceContextStructP, double * hostAP, int elementNum, double alpha ){
int sizeA = elementNum * sizeof(double);
//
double * deviceA;
cudaMalloc( (void **) &deviceA, sizeA );
//
cudaMemcpy( deviceA, hostAP, sizeA, cudaMemcpyHostToDevice );
//
int threadsPerBlock = getMaxThreadsPerMultiProcessor( deviceContextStructP );
int blocksPerGrid = ( elementNum + threadsPerBlock - 1 ) / threadsPerBlock;
arrayAddKernel<<< blocksPerGrid, threadsPerBlock >>>( deviceA, alpha, elementNum );
//
cudaMemcpy( hostAP, deviceA, sizeA, cudaMemcpyDeviceToHost );
cudaFree(deviceA);
}
//subtractArray()
__global__ void subtractArrayKernel( double alpha, double *deviceA, int elementNum ){
int i = blockDim.x * blockIdx.x + threadIdx.x;
if( i < elementNum) {
deviceA[i] = alpha - deviceA[i];
}
}
void subtractArray( deviceContextStruct * deviceContextStructP, double alpha, double * hostAP, int elementNum ){
int sizeA = elementNum * sizeof(double);
//
double * deviceA;
cudaMalloc( (void **) &deviceA, sizeA );
//
cudaMemcpy( deviceA, hostAP, sizeA, cudaMemcpyHostToDevice );
//
int threadsPerBlock = getMaxThreadsPerMultiProcessor( deviceContextStructP );
int blocksPerGrid = ( elementNum + threadsPerBlock - 1 ) / threadsPerBlock;
subtractArrayKernel<<< blocksPerGrid, threadsPerBlock >>>( alpha, deviceA, elementNum );
//
cudaMemcpy( hostAP, deviceA, sizeA, cudaMemcpyDeviceToHost );
cudaFree(deviceA);
}
//arrayMultiply()
__global__ void arrayMultiplyKernel( double *deviceA, double alpha, int elementNum ){
int i = blockDim.x * blockIdx.x + threadIdx.x;
if( i < elementNum) {
deviceA[i] = deviceA[i] * alpha;
}
}
void arrayMultiply( deviceContextStruct * deviceContextStructP, double * hostAP, int elementNum, double alpha ){
int sizeA = elementNum * sizeof(double);
//
double * deviceA;
cudaMalloc( (void **) &deviceA, sizeA );
//
cudaMemcpy( deviceA, hostAP, sizeA, cudaMemcpyHostToDevice );
//
int threadsPerBlock = getMaxThreadsPerMultiProcessor( deviceContextStructP );
int blocksPerGrid = ( elementNum + threadsPerBlock - 1 ) / threadsPerBlock;
arrayMultiplyKernel<<< blocksPerGrid, threadsPerBlock >>>( deviceA, alpha, elementNum );
//
cudaMemcpy( hostAP, deviceA, sizeA, cudaMemcpyDeviceToHost );
cudaFree(deviceA);
}
//divideArray()
__global__ void divideArrayKernel( double alpha, double *deviceA, int elementNum ){
int i = blockDim.x * blockIdx.x + threadIdx.x;
if( i < elementNum) {
deviceA[i] = alpha / deviceA[i];
}
}
void divideArray( deviceContextStruct * deviceContextStructP, double alpha, double * hostAP, int elementNum ){
int sizeA = elementNum * sizeof(double);
//
double * deviceA;
cudaMalloc( (void **) &deviceA, sizeA );
//
cudaMemcpy( deviceA, hostAP, sizeA, cudaMemcpyHostToDevice );
//
int threadsPerBlock = getMaxThreadsPerMultiProcessor( deviceContextStructP );
int blocksPerGrid = ( elementNum + threadsPerBlock - 1 ) / threadsPerBlock;
divideArrayKernel<<< blocksPerGrid, threadsPerBlock >>>( alpha, deviceA, elementNum );
//
cudaMemcpy( hostAP, deviceA, sizeA, cudaMemcpyDeviceToHost );
cudaFree(deviceA);
}
//arrayPower()
__global__ void arrayPowerKernel( double *deviceA, double alpha, int elementNum ){
int i = blockDim.x * blockIdx.x + threadIdx.x;
if( i < elementNum) {
deviceA[i] = pow( deviceA[i], alpha );
}
}
void arrayPower( deviceContextStruct * deviceContextStructP, double * hostAP, int elementNum, double alpha ){
int sizeA = elementNum * sizeof(double);
//
double * deviceA;
cudaMalloc( (void **) &deviceA, sizeA );
//
cudaMemcpy( deviceA, hostAP, sizeA, cudaMemcpyHostToDevice );
//
int threadsPerBlock = getMaxThreadsPerMultiProcessor( deviceContextStructP );
int blocksPerGrid = ( elementNum + threadsPerBlock - 1 ) / threadsPerBlock;
arrayPowerKernel<<< blocksPerGrid, threadsPerBlock >>>( deviceA, alpha, elementNum );
//
cudaMemcpy( hostAP, deviceA, sizeA, cudaMemcpyDeviceToHost );
cudaFree(deviceA);
}
//arraySquareRoot()
__global__ void arraySquareRootKernel( double *deviceA, int elementNum ){
int i = blockDim.x * blockIdx.x + threadIdx.x;
if( i < elementNum) {
deviceA[i] = sqrt( deviceA[i] );
}
}
void arraySquareRoot( deviceContextStruct * deviceContextStructP, double * hostAP, int elementNum ){
int sizeA = elementNum * sizeof(double);
//
double * deviceA;
cudaMalloc( (void **) &deviceA, sizeA );
//
cudaMemcpy( deviceA, hostAP, sizeA, cudaMemcpyHostToDevice );
//
int threadsPerBlock = getMaxThreadsPerMultiProcessor( deviceContextStructP );
int blocksPerGrid = ( elementNum + threadsPerBlock - 1 ) / threadsPerBlock;
arraySquareRootKernel<<< blocksPerGrid, threadsPerBlock >>>( deviceA, elementNum );
//
cudaMemcpy( hostAP, deviceA, sizeA, cudaMemcpyDeviceToHost );
cudaFree(deviceA);
}
//arrayCubeRoot()
__global__ void arrayCubeRootKernel( double *deviceA, int elementNum ){
int i = blockDim.x * blockIdx.x + threadIdx.x;
if( i < elementNum) {
deviceA[i] = cbrt( deviceA[i] );
}
}
void arrayCubeRoot( deviceContextStruct * deviceContextStructP, double * hostAP, int elementNum ){
int sizeA = elementNum * sizeof(double);
//
double * deviceA;
cudaMalloc( (void **) &deviceA, sizeA );
//
cudaMemcpy( deviceA, hostAP, sizeA, cudaMemcpyHostToDevice );
//
int threadsPerBlock = getMaxThreadsPerMultiProcessor( deviceContextStructP );
int blocksPerGrid = ( elementNum + threadsPerBlock - 1 ) / threadsPerBlock;
arrayCubeRootKernel<<< blocksPerGrid, threadsPerBlock >>>( deviceA, elementNum );
//
cudaMemcpy( hostAP, deviceA, sizeA, cudaMemcpyDeviceToHost );
cudaFree(deviceA);
}
//logEArray()
__global__ void logEArrayKernel( double *deviceA, int elementNum ){
int i = blockDim.x * blockIdx.x + threadIdx.x;
if( i < elementNum) {
deviceA[i] = log( deviceA[i] );
}
}
void logEArray( deviceContextStruct * deviceContextStructP, double * hostAP, int elementNum ){
int sizeA = elementNum * sizeof(double);
//
double * deviceA;
cudaMalloc( (void **) &deviceA, sizeA );
//
cudaMemcpy( deviceA, hostAP, sizeA, cudaMemcpyHostToDevice );
//
int threadsPerBlock = getMaxThreadsPerMultiProcessor( deviceContextStructP );
int blocksPerGrid = ( elementNum + threadsPerBlock - 1 ) / threadsPerBlock;
logEArrayKernel<<< blocksPerGrid, threadsPerBlock >>>( deviceA, elementNum );
//
cudaMemcpy( hostAP, deviceA, sizeA, cudaMemcpyDeviceToHost );
cudaFree(deviceA);
}
//log2Array()
__global__ void log2ArrayKernel( double *deviceA, int elementNum ){
int i = blockDim.x * blockIdx.x + threadIdx.x;
if( i < elementNum) {
deviceA[i] = log2( deviceA[i] );
}
}
void log2Array( deviceContextStruct * deviceContextStructP, double * hostAP, int elementNum ){
int sizeA = elementNum * sizeof(double);
//
double * deviceA;
cudaMalloc( (void **) &deviceA, sizeA );
//
cudaMemcpy( deviceA, hostAP, sizeA, cudaMemcpyHostToDevice );
//
int threadsPerBlock = getMaxThreadsPerMultiProcessor( deviceContextStructP );
int blocksPerGrid = ( elementNum + threadsPerBlock - 1 ) / threadsPerBlock;
log2ArrayKernel<<< blocksPerGrid, threadsPerBlock >>>( deviceA, elementNum );
//
cudaMemcpy( hostAP, deviceA, sizeA, cudaMemcpyDeviceToHost );
cudaFree(deviceA);
}
//log10Array()
__global__ void log10ArrayKernel( double *deviceA, int elementNum ){
int i = blockDim.x * blockIdx.x + threadIdx.x;
if( i < elementNum) {
deviceA[i] = log10( deviceA[i] );
}
}
void log10Array( deviceContextStruct * deviceContextStructP, double * hostAP, int elementNum ){
int sizeA = elementNum * sizeof(double);
//
double * deviceA;
cudaMalloc( (void **) &deviceA, sizeA );
//
cudaMemcpy( deviceA, hostAP, sizeA, cudaMemcpyHostToDevice );
//
int threadsPerBlock = getMaxThreadsPerMultiProcessor( deviceContextStructP );
int blocksPerGrid = ( elementNum + threadsPerBlock - 1 ) / threadsPerBlock;
log10ArrayKernel<<< blocksPerGrid, threadsPerBlock >>>( deviceA, elementNum );
//
cudaMemcpy( hostAP, deviceA, sizeA, cudaMemcpyDeviceToHost );
cudaFree(deviceA);
}
//hadamardProduct()
__global__ void hadamardProductKernel( double * deviceA, double * deviceB, int elementNum ){
int i = blockDim.x * blockIdx.x + threadIdx.x;
if( i < elementNum ){
deviceA[i] = deviceA[i] * deviceB[i];
}
}
void hadamardProduct( deviceContextStruct * deviceContextStructP, double * hostAP, double * hostBP, int elementNum ){
int sizeA = elementNum * sizeof(double);
//
double * deviceA, * deviceB;
cudaMalloc( (void **) &deviceA, sizeA );
cudaMalloc( (void **) &deviceB, sizeA );
//
cudaMemcpy( deviceA, hostAP, sizeA, cudaMemcpyHostToDevice );
cudaMemcpy( deviceB, hostBP, sizeA, cudaMemcpyHostToDevice );
//
int threadsPerBlock = getMaxThreadsPerMultiProcessor( deviceContextStructP );
int blocksPerGrid = ( elementNum + threadsPerBlock - 1 ) / threadsPerBlock;
hadamardProductKernel<<< blocksPerGrid, threadsPerBlock >>>( deviceA, deviceB, elementNum );
//
cudaMemcpy( hostAP, deviceA, sizeA, cudaMemcpyDeviceToHost );
cudaFree(deviceA);
}
//transpose()
__global__ void transposeKernel( double *deviceA, int elementNum, int heightA, int widthA, double *deviceB ){
int i = blockDim.x * blockIdx.x + threadIdx.x;
if( i < elementNum ) {
int bI = i / heightA;
int bJ = i % heightA;
deviceB[i] = deviceA[ bJ * widthA + bI ];
}
}
void transpose( deviceContextStruct * deviceContextStructP, double * hostAP, int elementNum, int heightA, int wigthA ){
int sizeA = elementNum * sizeof(double);
//
double * deviceA, * deviceB;
cudaMalloc( (void **) &deviceA, sizeA );
cudaMalloc( (void **) &deviceB, sizeA );
//
cudaMemcpy( deviceA, hostAP, sizeA, cudaMemcpyHostToDevice );
//
int threadsPerBlock = getMaxThreadsPerMultiProcessor( deviceContextStructP );
int blocksPerGrid = ( elementNum + threadsPerBlock - 1 ) / threadsPerBlock;
transposeKernel<<< blocksPerGrid, threadsPerBlock >>>( deviceA, elementNum, heightA, wigthA, deviceB );
//
cudaMemcpy( hostAP, deviceB, sizeA, cudaMemcpyDeviceToHost );
cudaFree(deviceA);
cudaFree(deviceB);
}
/*
* Matrix is a PHP extension. It can do parallel computing base on CUDA.
*
* GitHub: https://github.com/BourneSuper/matrix
*
* Author: Bourne Wong <cb44606@gmail.com>
*
* */
|
18,800 |
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
__global__
void add(int n, float *x, float *y)
{
int index = blockIdx.x * blockDim.x + threadIdx.x;
int stride = blockDim.x * gridDim.x;
for (int i = index; i < n; i += stride) {
y[i] = x[i] + y[i];
}
}
int main(void) {
int N = 1 << 20;
float *x, *y;
// Allocate Unified Memory accessible from CPU or GPU
cudaMallocManaged(&x, N*sizeof(float));
cudaMallocManaged(&y, N*sizeof(float));
// initialize x and y arrays on the host
for (int i = 0; i < N; i++) {
x[i] = 1.0f;
y[i] = 2.0f;
}
int blockSize = 256;
int numBlocks = (N + blockSize - 1) / blockSize;
printf("Runs with %d blocks \n", numBlocks);
// Run kernel on 1M elements on the GPU
add <<<numBlocks, blockSize>>>(N, x, y);
// Wait for GPU to finish before accessing on host
cudaDeviceSynchronize();
for (int i = 0; i < 10; i++) {
printf("[%d] is %f \n", i, y[i]);
}
cudaFree(x);
cudaFree(y);
return 0;
} |
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